Sticker  recommendation method and apparatus

ABSTRACT

Aspects of the disclosure provide methods and apparatuses for recommending a sticker set. An apparatus for recommending a sticker set includes interface circuitry and processing circuitry. When the interface circuitry receives a sticker recommendation request from a terminal, the processing circuitry determines a historical sticker set that includes a sticker previously sent by a user of the terminal device, and at least one recommendable sticker set not including the historical sticker set. Then the processing circuitry determines a recommendation index for each of the at least one recommendable sticker set according to an emotion feature of the historical sticker set and an emotion feature of the respective recommendable sticker set. According to the recommendation index for each of the at least one recommendable sticker set, the interface circuitry sends a sticker set recommendation for one or more of the at least one recommendable sticker set to the terminal device.

RELATED APPLICATION

This application is a continuation of International Application No.PCT/CN2018/074136, filed on Jan. 25, 2018, which claims priority toChinese Patent Application No. 201710069474.5, entitled “STICKERRECOMMENDATION METHOD AND APPARATUS” filed on Feb. 8, 2017, and ChinesePatent Application No. 201710075877.0, entitled “STICKER RECOMMENDATIONMETHOD AND APPARATUS” filed on Feb. 13, 2017. The entire disclosures ofthe prior applications are hereby incorporated by reference in theirentirety.

FIELD OF THE TECHNOLOGY

This application relates to the field of network communicationstechnologies.

BACKGROUND OF THE DISCLOSURE

In Internet communication, stickers for interaction are used for helpingusers to more accurately express information (for example, a mood or astatus). For example, in an instant messaging process, a sender ofinstant messaging may send a sticker to a receiver as a session message.

With development of network technologies, there are more sticker setsincluding stickers in an Internet platform. To enable a user to find anavailable sticker set in time, the Internet platform recommends asticker set to the user. Currently, the Internet platform generallypreferentially recommends, to a user according to a usage popularity ofsticker sets, a sticker set that is relatively popular. However,different sticker sets include different stickers, and different userslike different stickers. Therefore, a sticker set recommended to a useraccording to the usage popularity may not be suitable for the user andconsequently is not of interested to the user, and a sticker setsuitable for the user cannot be found by the user in time, causing awaste of a sticker set resource in the Internet platform.

SUMMARY

Aspects of the disclosure provide methods and apparatuses forrecommending a sticker set. In some examples, an apparatus forrecommending a sticker set includes interface circuitry and processingcircuitry.

When the interface circuitry receives a sticker recommendation requestfrom a terminal, the processing circuitry determines a historicalsticker set that includes a sticker previously sent by a user of theterminal device, and at least one recommendable sticker set notincluding the historical sticker set. Then the processing circuitrydetermines a recommendation index for each of the at least onerecommendable sticker set according to an emotion feature of thehistorical sticker set and an emotion feature of the respectiverecommendable sticker set. According to the recommendation index foreach of the at least one recommendable sticker set, the interfacecircuitry sends a sticker set recommendation for one or more of the atleast one recommendable sticker set to the terminal device.

In various embodiments, when the at least one recommendable sticker setincludes a plurality of recommendable sticker sets, the processingcircuitry calculates a similarity between each of the recommendablesticker sets and the historical sticker set according to the emotionfeature of the respective recommendable sticker set and the emotionfeature of the historical sticker set. The processing circuitry furtherdetermines recommendation indexes of the plurality of recommendablesticker sets according to the similarities between the plurality ofrecommendable sticker sets and the historical sticker set.

According to an aspect of the disclosure, the emotion feature of thehistorical sticker set is determined according to an emotion feature ofthe sticker in the historical sticker set, the emotion feature of thesticker is a feature extracted from the sticker for reflecting anemotion status presented by the sticker.

In an embodiment, the processing circuitry determines an emotion featureof each sticker in the historical sticker set. According to an averagevalue of the emotion features of the stickers in the historical stickerset, the processing circuitry determines the emotion feature of thehistorical sticker set

In an embodiment, the processing circuitry determines a total quantityof usage times of each sticker in the historical sticker set and selectsa specified quantity of stickers from the historical sticker set basedon the total quantities of usage times. Then the processing circuitrydetermines a weight of each selected sticker of the specified quantityof stickers according to the total quantity of usage times of therespective selected sticker. According to the weights of the selectedstickers, the processing circuitry performs a weighted summation onemotion features of the selected stickers and determines the emotionfeature of the historical sticker set based on a result of the weightedsummation.

In an embodiment, the processing circuitry calculates a comprehensivesimilarity score of each of the recommendable sticker sets according tothe similarity between the recommendable sticker sets and the historicalsticker set. According to the comprehensive similarity scores of theplurality of recommendable sticker sets, the processing circuitrydetermines the recommendation indexes of the plurality of recommendablesticker sets.

In an embodiment, the processing circuitry sums similarities between oneof the plurality of recommendable sticker sets and each of a pluralityof historical sticker sets, the plurality of historical sticker setsincluding the historical sticker set. Based on the summed similarities,the processing circuitry determines the comprehensive similarity scoreof the one of the plurality of recommendable sticker sets.

In an embodiment, the at least one recommendable sticker set includes aplurality of recommendable stocker set. According to a plurality ofemotion features of the plurality of recommendable sticker sets, theprocessing circuitry clusters the plurality of recommendable stickersets into a plurality of classifications. Accordingly, each of theplurality of recommendable sticker sets corresponds to one of theplurality of classifications and each of the plurality ofclassifications includes at least one of the plurality of recommendablesticker sets. The processing circuitry determines a classification towhich the historical sticker set belongs in the plurality ofclassifications and selects the at least one recommendable sticker setfrom the at least one of the plurality of recommendable sticker setsincluded in the classification to which the historical sticker setbelongs.

In an embodiment, the processing circuitry receives a to-be-postedsticker set, extracts an emotion feature of the to-be-posted stickerset, calculate a similarity between the to-be-posted sticker set andeach stored sticker set and determine whether each of the similaritiesbetween the to-be-posted sticker set and each stored sticker set is lessthan a preset threshold. When each of the similarities between theto-be-posted sticker set and each stored sticker set is determined to beless than the preset threshold, the processing circuitry stores theto-be-posted sticker set.

In an embodiment, the emotion feature indicates an image style of thesticker in the historical sticker set, one sticker in the at least onerecommendable sticker is a specified sticker, each sticker in thehistorical sticker set has an image style such that the historicalsticker set has at least one image style. The processing circuitrydetermines a usage record of the historical sticker set by the user, anuncorrected recommendation index of the specified sticker and an imagestyle of the specified sticker. According to the usage record of thehistorical sticker set by the user, the at least one image style of thehistorical sticker set, the uncorrected recommendation index of thespecified sticker, and the image style of the specified sticker, theprocessing circuitry determines a corrected recommendation index of thespecified sticker. The interface circuitry sends the specified stickerto the terminal device when the corrected recommendation index of thespecified sticker satisfies a recommendation condition.

In an embodiment, the processing circuitry generates an interest vectorof the user according to the usage record of the historical sticker setby the user and the at least one image style of the historical stickerset such that each of the at least one image style of the historicalsticker set corresponds to an element of the interest vector. Theprocessing circuitry further determines the corrected recommendationindex of the specified sticker according to the interest vector of theuser, the image style of the specified sticker, and a preset correctionformula including the uncorrected recommended index of the specifiedsticker.

In an embodiment, the usage record includes a quantity of usage times ofthe historical sticker set by the user. The processing circuitrygenerates an initialized interest vector according to a quantity ofimage styles including the at least one image style of the historicalsticker set such that each element of the initialized interest vectorcorresponds to one of the quantity of image styles. For each of the atleast one image style of the historical sticker set, the processingcircuitry adds the quantity of usage times of the historical sticker setby the user to an element of the initialized interest vector thatcorresponds to the respective image style and generates the interestvector of the user according to a normalization of the initializedinterest vector.

In an embodiment, the processing circuitry determines the uncorrectedrecommendation index of the specified sticker according to acollaborative-filtering-based recommendation algorithm.

In an embodiment, the processing circuitry extracts image featureinformation of a plurality of sample stickers and the historicalsticker, the image feature information of the plurality of samplestickers corresponding to a plurality of image styles. According to theimage feature information of the plurality of sample stickers and theplurality of image styles, the processing circuitry determines amachine-learning classification model. The processing circuitry furtherdetermines the image style of the historical sticker according to anoutput of the machine-learning classification model with an input ofimage feature information of the historical sticker.

Aspects of the disclosure also provide a non-transitorycomputer-readable medium storing instructions which when executed by acomputer for recommending a sticker set cause the computer to performthe method for recommending a sticker set.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic architectural composition diagram of a stickerrecommendation system according to an embodiment of this application.

FIG. 2 is a schematic flowchart of an embodiment of a stickerrecommendation method.

FIG. 3 is a schematic flowchart of an implementation of obtaining anemotion feature of a sticker according to an embodiment of thisapplication.

FIG. 4 is a schematic flowchart of extracting an emotion feature of asticker by using a convolutional neural network model according to anembodiment of this application.

FIG. 5 is a schematic flowchart of an implementation of determining anemotion feature of a sticker set according to an embodiment of thisapplication.

FIG. 6 is a schematic flowchart of a sticker recommendation method in anapplication scenario according to an embodiment of this application.

FIG. 7 is a schematic diagram of an interface including a stickerrecommendation button in an instant messaging application according toan embodiment of this application.

FIG. 8 is an exemplary schematic diagram of a sticker recommendationinterface presented in a terminal according to a sticker set recommendedby a server.

FIG. 9 is a schematic flowchart of another embodiment of a stickerrecommendation method.

FIG. 10 is an exemplary schematic diagram of a function graph in theembodiment shown in FIG. 9.

FIG. 11 is a schematic flowchart of another embodiment of a stickerrecommendation method.

FIG. 12 is a schematic diagram of an implementation process ofrecommending a sticker to a user by a server cluster according to anembodiment of this application.

FIG. 13 is a schematic structural composition diagram of a stickerrecommendation apparatus according to an embodiment of this application.

FIG. 14 is a schematic structural composition diagram of a serveraccording to an embodiment of this application.

FIG. 15 is another schematic structural composition diagram of a stickerrecommendation apparatus according to an embodiment of this application.

FIG. 16 is another schematic structural composition diagram of a serveraccording to an embodiment of this application.

DESCRIPTION OF EMBODIMENTS

Embodiments of this application provide a sticker recommendation methodand apparatus, to more properly recommend a sticker set to a user,thereby improving utilization of a sticker set resource, and avoiding awaste of the sticker set resource. It is noted that the stickerrecommendation method can be used to recommend other graphicalrepresentations (e.g., emojis) in other embodiments.

Terms such as “first”, “second”, “third”, and “fourth” (if the termsexist) in this specification, claims, and accompanying drawings of thisapplication are used for distinguishing between similar objects insteadof describing a particular sequence or order. It is understood that datatermed in such a way are interchangeable in appropriate circumstances,so that the embodiments described herein can be implemented in an orderother than an order illustrated or described herein. In addition, terms“include”, “possess”, and any variant thereof are intended to covernon-exclusive inclusion. For example, a process, method, system,product, or device that includes a list of steps or units does not needto be limited to the clearly listed steps or units, but may includeother steps or units not clearly listed or inherent to the process,method, product, or device.

A sticker recommendation method in the embodiments of this applicationmay be applied to a sticker recommendation in an Internet platform, forexample, a sticker recommendation in an instant messaging application,or a sticker recommendation in scenarios such as a forum or a microblog.

FIG. 1 is a schematic architectural composition diagram of a stickerrecommendation system according to an embodiment of this application.The system may include a service platform 10 and at least one terminal11.

The service platform 10 may include at least one server 101.

In an embodiment, to improve processing efficiency of the serviceplatform 10 for processing a sticker recommendation request, the serviceplatform 10 may include a server cluster including a plurality ofservers 101.

It may be understood that a sticker set in the service platform 10 maybe stored in the server or may be stored in a database. In anembodiment, the service platform may further include a database 102. Thedatabase 102 may store the sticker set in the service platform 10, ormay store other data related to the service platform 10.

The terminal 11 is configured to send a sticker recommendation requestto the server 101 of the service platform 10.

Correspondingly, the server 101 of the service platform 10 is configuredto determine, in response to the sticker recommendation request, asticker set list to be recommended to the terminal 11, and return thesticker set list, for example that lists at least one sticker set, tothe terminal 11.

In an embodiment, the terminal 11 may be a client running an applicationin which sticker exchange is required. For example, the terminal 11 maybe a client running an instant messaging application. Correspondingly,the service platform 10 may be a service platform for instant messaging,and the server 11 may be a server proving an instant messaging serviceor a server providing a sticker service related to instant messaging.

In an embodiment, the terminal 11 may be a client in which a browser islocated. In the embodiment, the terminal 11 may log in to the serviceplatform 10 by using the browser, to communicate with another networkuser based on the service platform 10. For example, the terminal 11 maylog in to a service platform of a microblog by using the browser, tobrowse and comment on microblog content posted by another user or leavea message to another user. In the process of commenting on microblogcontent or leaving a message to another user, a user may select, in acomment bar or a message leaving bar on a microblog page by using thebrowser, a sticker provided by the service platform 10. The sticker maybe recommended by the service platform 10 to the user.

It is noted that a scenario in which the server 101 of the serviceplatform 10 may recommend a sticker set to the terminal 11 may furtherinclude other cases. This is not limited herein.

It may be understood that, the terminal 11 may be a device that canaccess the service platform 10 in a scenario. For example, the terminal11 may be a mobile phone, a tablet computer, or a desktop computer.

FIG. 2 is a schematic flowchart of an embodiment of a stickerrecommendation method. The method in this embodiment may include thefollowing steps:

In step S201, a user logs in to a server (e.g., the server 101) by usinga terminal (e.g., the terminal 11).

For example, the user may log in to a server of a forum by using abrowser of the terminal, to access a forum page provided by the server.In another example, the user may log in to a server of an instantmessaging application by using a terminal of the instant messagingapplication.

Step S201 is an optional step, and is intended for ease of understandinga specific procedure of this embodiment of this application. However, itmay be understood that, after logging in to the server by using theterminal, the user does not need to repeatedly log in to the serverevery time if subsequently needing to send a sticker recommendationrequest to the server.

In step S202, the terminal sends a sticker recommendation request to theserver, where the sticker recommendation request carries an identifierof the user.

The sticker recommendation request is used for requesting the server torecommend a sticker set.

The sticker recommendation request may be triggered when the terminaldetects that a current operation of the user satisfies a condition ofrecommending a sticker to the user.

For example, when detecting that the user requests to open a stickerselection list for the user to select a sticker, the terminal may send asticker list exhibition request to the server as the stickerrecommendation request. For example, when the user taps a sticker buttonbelow a comment bar of the forum, the terminal may send the stickerrecommendation request to the server, so that the server recommends andreturns a sticker set to the terminal, and then the terminal exhibits,to the user, the recommended sticker set, including a plurality ofstickers for example, that may be selected by the user along with asticker in the sticker set.

In another example, when detecting that the user taps a stickerrecommendation option or requests to enter a sticker recommendationpage, the terminal may generate the sticker recommendation request andsend the sticker recommendation request to the server. For example, asticker recommendation store or a sticker recommendation button is setin some applications. If the user requests to access the stickerrecommendation store or taps the sticker recommendation button, theterminal may send the sticker recommendation request to the server, sothat the server returns a sticker recommendation page such as a stickerstore page including a recommended sticker set.

To help the server to identify a user that requests the server torecommend a sticker set, the sticker recommendation request may carrythe identifier such as a user name, an account, or a phone number of theuser. It is noted that adding the identifier of the user to the stickerrecommendation request is a manner of helping the server to identify theuser. In other embodiments, alternatively, the user sending the stickerrecommendation request may be identified according to a communicationchannel allocated by the server to the user when the user logs in to theserver. It is noted the user sending the sticker recommendation requestmay alternatively be identified in other manners. This is not limitedherein.

In step S203, the server determines, in response to the stickerrecommendation request, a set of one or more historical stickers sent bythe user.

The set of historical stickers includes at least one historical sticker.

In step S204, the server determines at least one historical sticker setto which at least one historical sticker included in the set ofhistorical stickers belongs.

For ease of distinguishing, in this application, a sticker sent by theuser before a current time point is referred to as the historicalsticker. Correspondingly, a sticker set to which the historical stickerbelongs is referred to as the historical sticker set.

It may be understood that, one sticker set may include one or morestickers, and there is an association between stickers belonging to asame sticker set. For example, the stickers reflect the same subjectcontent. A sticker in a sticker set may be a single frame of an image.For example, the sticker may include static image content.Alternatively, a sticker may be a consecutive animation, a short video,or the like. This is not limited herein.

In step S205, for a historical sticker set, the server obtains anemotion feature of each historical sticker in the historical stickerset.

An emotion feature of a sticker is, for example, a feature extractedfrom the sticker for reflecting an emotion status presented by thesticker. It may be understood that, an emotion feature of each stickercan be a vector. Dimensions of the vector may be set as required. Forexample, the emotion feature may be a vector of 1×4096 dimensions.

The emotion feature of the sticker may be obtained in real time, or maybe pre-obtained and then stored. For example, the emotion feature ofeach historical sticker in the historical sticker set may bepre-extracted and then stored in the server or the database.

It may be understood that, extracting the emotion feature of the stickermay be extracting an image feature of the sticker and extracting afeature that can reflect an emotion included in the sticker. The imagefeature of the sticker may be extracted in a plurality of featureextraction manners. For example, the sticker may be input into apre-configured feature extraction model, to extract the emotion featureof the sticker. For ease of understanding, FIG. 3 is a schematicflowchart of extracting an emotion feature of a sticker according tothis application. It may be learned from FIG. 3 that:

In step S301, a pre-trained convolutional neural network model isloaded. For example, a Visual Geometry Group (VGG) convolutional neuralnetwork model may be loaded.

In t step S302, the sticker is input into the convolutional neuralnetwork model, and the emotion feature of the sticker that is output bythe convolutional neural network model is obtained.

After the sticker is input into the convolutional neural network model,a feedforward transmission is performed on the sticker in theconvolutional neural network, and the sticker sequentially passesthrough a convolution layer and a full connection layer of theconvolutional neural network model. As shown in FIG. 4, theconvolutional neural network includes the convolution layer of C1-C4 andthe full connection layer corresponding to fc6, fc7, and fc8. It may belearned from FIG. 4 that, an “Ali” sticker is input into theconvolutional neural network model, sequentially passes through theconvolution layer indicated by C1, C2, C3, and C4, and then passesthrough the full connection layer of fc6 and fc7. After the stickerpasses through the full connection layer of fc7, an image feature of thesticker is output. The image feature is an emotion feature of thesticker.

It is noted that, when the emotion feature of the sticker ispre-extracted, the sticker may be stored in the server or the databaseafter the server extracts the emotion feature of the sticker; or adevice other than the server pre-extracts the emotion feature of thesticker, and transmits the emotion feature to the server or stores theemotion feature to the database.

In step S206, the server calculates an average value of the emotionfeatures of all the historical stickers in the historical sticker set,and uses the average value as an emotion feature of the historicalsticker set.

In an embodiment, for a sticker set, an emotion feature of the stickerset may be represented as:

$\begin{matrix}{{\frac{1}{n}{\sum\limits_{i = 0}^{n}x_{i}}},} & \left( {{formula}\mspace{14mu} 1} \right)\end{matrix}$

where

x_(i) is an emotion feature of a sticker i in the sticker set, x_(i) isa vector, and n is a total quantity of stickers in the sticker set.

Correspondingly, the emotion feature of the historical sticker set maybe calculated based on the formula 1 and with reference to the emotionfeature of each historical sticker in the historical sticker set.

It is noted that, during determining of an emotion feature of a stickerset (e.g., the historical sticker set in step S206), the emotion featureof the sticker set may be calculated in real time in the mannerdescribed in step S205 and step S206. However, in an embodiment, tofurther improve efficiency of determining the emotion feature of thesticker set, the server may pre-calculate and store an emotion featureof each sticker set. For example, the emotion feature of the sticker setmay be pre-calculated by the server, and stored in the server or thedatabase. In this way, when emotion features of one or more sticker setsneed to be determined, emotion features of a plurality of stored stickersets may be queried, to obtain the emotion feature of the sticker set.

Correspondingly, for the historical sticker set, the emotion feature ofthe historical sticker set may be obtained from the emotion features ofthe plurality of stored sticker sets.

In step S207, the server determines sticker sets in a sticker set listother than the historical sticker set as a plurality of recommendableto-be-recommended sticker sets.

The sticker set list includes all available sticker sets in the serviceplatform.

A sticker-set set not including the historical sticker set may bedetermined by using the sticker set list, and a plurality of stickersets included in the sticker-set set is used as the to-be-recommendedsticker sets.

It is noted that, maintaining the sticker set in the server (or theservice platform) by using the sticker set list is an exemplaryimplementation. The server may maintain all the sticker sets by using aset or in other manners according to other embodiments.

It may be understood that, using the sticker sets other than thehistorical sticker set as the to-be-recommended sticker sets is anexemplary manner of determining the to-be-recommended sticker sets. Thismanner may be applicable to recommending, to the user, a sticker setthat the user has not used and that is suitable for the user. Forexample, the user needs to download a sticker set before using thesticker set, and a sticker set having been downloaded by the user doesnot need to be repeatedly recommended to the user.

However, the server may determine the plurality of recommendable stickersets in other manners according to other embodiments. For example, theserver may use all the sticker sets (which may include a sticker sethaving been used by the user) in the server as the to-be-recommendedsticker sets. This manner is applicable to a scenario in which the userenters a sticker in real time. For example, the user does not need todownload a sticker set for use. After each time the server recommends asticker set to the user, the user may directly use a sticker in thesticker set for network communication.

In step S208, for a to-be-recommended sticker set, the server obtains anemotion feature of each sticker in the to-be-recommended sticker set.

In step S209, the server calculates an average value of the emotionfeatures of all the stickers in the to-be-recommended sticker set, anduses the average value as an emotion feature of the to-be-recommendedsticker set.

Step S208 and step S209 are an exemplary process of determining theemotion feature of the to-be-recommended sticker set by the user. Forthe process, refer to the related descriptions of step S205 and 5206,and details are not described herein again.

Correspondingly, in this application, for ease of understanding, theexample in which the server calculates the emotion feature of theto-be-recommended sticker set in real time is used for description.However, it may be understood that, the server may query the emotionfeature of each pre-stored sticker set for the emotion feature of theto-be-recommended sticker set, to directly obtain the emotion feature ofthe to-be-recommended sticker set.

In step S210, for each historical sticker set, the server calculates asimilarity between each to-be-recommended sticker set and the historicalsticker set according to the emotion feature of each to-be-recommendedsticker set and the emotion feature of the historical sticker set.

That is, for a to-be-recommended sticker set, a similarity between theto-be-recommended sticker set and each historical sticker set needs tobe calculated.

For example, assuming that the user sends five historical stickers, thefive historical stickers belong to three historical sticker sets thatare respectively a historical sticker set A, a historical sticker set B,and a historical sticker set C, and there are 20 recommendableto-be-recommended sticker sets, a similarity between each of the 20to-be-recommended sticker sets and the historical sticker set A needs tobe calculated. Correspondingly, a similarity between each of the 20to-be-recommended sticker sets and the historical sticker set B and asimilarity between each of the 20 to-be-recommended sticker sets and thehistorical sticker set C are calculated.

It may be understood that, there may be a plurality of manners ofdetermining the similarity between the to-be-recommended sticker set andthe historical sticker set according to the emotion feature of theto-be-recommended sticker set and the emotion feature of the historicalsticker set. For example, a cosine similarity between the emotionfeature of the to-be-recommended sticker set and the emotion feature ofthe historical sticker set may be calculated, to obtain the similaritybetween the to-be-recommended sticker set and the historical stickerset. It is noted that a Euclidean distance, a Manhattan distance, or thelike between the emotion feature of the to-be-recommended sticker setand the emotion feature of the historical sticker set may be calculated,to obtain the similarity between the to-be-recommended sticker set andthe historical sticker set. This is not limited herein.

In step S211, for a to-be-recommended sticker set, the server calculatesa comprehensive similarity score of the to-be-recommended sticker setrelative to the at least one historical sticker set according to thesimilarity between the to-be-recommended sticker set and each historicalsticker set.

The comprehensive similarity score is equivalent to a score that isdetermined according to the similarity between the to-be-recommendedsticker set and each historical sticker set and that represents a degreeof similarity between the to-be-recommended sticker set and all thehistorical sticker sets.

It may be understood that, there may be a plurality of implementationsof calculating the comprehensive similarity score of theto-be-recommended sticker set relative to all the sticker sets accordingto the similarity between the to-be-recommended sticker set and eachhistorical sticker set:

In an embodiment, the similarity between the to-be-recommended stickerset and each historical sticker set may be summed, and a summationresult is used as the comprehensive similarity score of theto-be-recommended sticker set relative to the at least one sticker set.An example is used for description. The historical sticker set includesa historical sticker set A, a historical sticker set B, and a historicalsticker set C. A comprehensive similarity score score(M_(i)) of ato-be-recommended sticker set M_(i) may be represented as follows:

score(M _(i))=sim(A,M _(i))+sim(B,M _(i))+sim(C,M _(i))   (formula 2),

where

sim(A,M_(i)) is a similarity between the to-be-recommended sticker setM_(i) and the historical sticker set A, sim(B,M_(i)) is a similaritybetween the to-be-recommended sticker set M_(i) and the historicalsticker set B, and sim(C,M_(i)) is a similarity between theto-be-recommended sticker set M_(i) and the historical sticker set C.

In an embodiment, an average value of the similarities between theto-be-recommended sticker set and all the historical sticker sets may becalculated according to the similarity between the to-be-recommendedsticker set and each historical sticker set, and the calculated averagevalue is used as the comprehensive similarity score of theto-be-recommended sticker set. Still using the formula 2 as an example,a value of score(M_(i)) may be divided by the total quantity 3 ofhistorical sticker sets, to obtain a similarity average value, and thesimilarity average value is used as the comprehensive similarity score.

In an embodiment, a total quantity of times of using the historicalsticker set by the user, that is, a total sum of quantities of times ofsending all the historical stickers in the historical sticker set by theuser, may be first determined, and then a weight of each historicalsticker set is determined according to the total quantity of times ofusing each historical sticker set; and for a to-be-recommended stickerset, weighted summation may be performed on the similarity between theto-be-recommended sticker set and each historical sticker set and theweight of the corresponding historical sticker set, and an obtainedsummation result may be determined as the comprehensive similarity scoreof the to-be-recommended sticker. Still using the example in which thehistorical sticker set includes the historical sticker sets A, B, and C,the comprehensive similarity score score(M) of the to-be-recommendedsticker set M_(i) may be represented as follows:

score(M _(i))=Q _(A) sim(A,M _(i))+Q _(B) sim(B,M _(i))+Q _(c) sim(C,M_(i))   (formula 3),

where

sim(A,M_(i)), sim(B,M_(i)), and sim(C,M_(i)) are respectively thesimilarity between the to-be-recommended sticker set M_(i) and each ofthe historical sticker set A, the historical sticker set B, and thehistorical sticker set C, and Q_(A), Q_(B), and Q_(c) are respectivelyweights of the historical sticker set A, the historical sticker set B,and the historical sticker set C.

It is noted that the comprehensive similarity score of theto-be-recommended sticker set may alternatively be determined in othermanners. This is not limited herein.

In step S212, the server determines a recommendation sequence of theplurality of to-be-recommended sticker sets according to a descendingorder of the comprehensive similarity scores.

For example, after the plurality of to-be-recommended sticker sets issorted according to the descending order of the comprehensive similarityscores of the to-be-recommended sticker sets, an obtained sequence maybe determined as the recommendation sequence.

It is noted that step S211 and step S212 are optional steps, and are anexemplary manner of determining the recommendation sequence of theto-be-recommended sticker sets. In an embodiment, after determining thesimilarity between the to-be-recommended sticker set and the historicalsticker set, the server may alternatively directly determine therecommendation sequence of the plurality of to-be-recommended stickersets according to the similarity between the to-be-recommended stickerset and the historical sticker set.

For example, priorities of different historical sticker sets are set.For example, a larger total quantity of times of using a historicalsticker set by the user indicates a higher priority of the historicalsticker set. Then, the recommendation sequence of the to-be-recommendedsticker sets is obtained according to the priorities and thesimilarities.

For example, it is assumed that the historical sticker set includeshistorical sticker sets A and B, and there are six to-be-recommendedsticker sets, namely, a to-be-recommended sticker set 1, ato-be-recommended sticker set 2, . . . , and a to-be-recommended stickerset 6. It is assumed that a descending sequence of similarities betweenthe six to-be-recommended sticker sets and the historical sticker set Amay be sequentially the to-be-recommended sticker set 6, theto-be-recommended sticker set 4, the to-be-recommended sticker set 3,the to-be-recommended sticker set 1, the to-be-recommended sticker set5, and the to-be-recommended sticker set 2; and a descending sequence ofsimilarities between the six to-be-recommended sticker sets and thehistorical sticker set B may be sequentially the to-be-recommendedsticker set 5, the to-be-recommended sticker set 2, theto-be-recommended sticker set 1, the to-be-recommended sticker set 6,the to-be-recommended sticker set 3, and the to-be-recommended stickerset 4.

In addition, assuming that the historical sticker set A has a higherpriority and the historical sticker set B has a lower priority, thedescending sequence of the similarities between the sixto-be-recommended sticker sets and the historical sticker set A may bedetermined as a recommendation sequence of the six to-be-recommendedsticker sets. Alternatively, the to-be-recommended sticker set 6 havinga highest similarity with the historical sticker set A ranks the firstin the recommendation sequence, then a to-be-recommended sticker sethaving a highest similarity with the historical sticker set B, namely,the to-be-recommended sticker set 5, is determined in unsortedto-be-recommended sticker sets to rank the second in the recommendationsequence, and a to-be-recommended sticker set having a highestsimilarity with the historical sticker set A, namely, theto-be-recommended sticker set 4, is determined in unsortedto-be-recommended sticker sets to rank the third in the recommendationsequence. The rest can be deduced by analogy, to obtain therecommendation sequence as: the to-be-recommended sticker set 6, theto-be-recommended sticker set 5, the to-be-recommended sticker set 4,the to-be-recommended sticker set 2, the to-be-recommended sticker set3, and the to-be-recommended sticker set 1.

S213. The server recommends the to-be-recommended sticker sets to theterminal based on the recommendation sequence.

It may be understood that, that the server recommends the sticker setsto the terminal according to a recommendation list may be: sending therecommendation sequence of the plurality of to-be-recommended stickersets to the terminal, to sequentially present an identifier of eachto-be-recommended sticker set on the terminal.

In an embodiment, considering that there may be relatively manyrecommendable sticker sets in the server other than the historicalsticker set, to reduce a data transmission amount and more properlyrecommend the sticker set to the user, a preset quantity of targetsticker sets that rank high in the recommendation sequence may beselected from the plurality of to-be-recommended sticker sets accordingto the recommendation sequence, and then the preset quantity of targetsticker sets are recommended to the terminal according to arecommendation sequence of the preset quantity of target sticker sets.The preset quantity may be set as required. For example, the presetquantity may be 20, 30, or the like.

In an embodiment, when the server recommends the preset quantity ofsticker sets to the terminal, to reduce a data computing amount, theterminal does not need to calculate the comprehensive similarity scoresof all the to-be-recommended sticker sets. Correspondingly, after thesimilarity between the to-be-recommended sticker set and each historicalsticker set is calculated in step S210, for each historical sticker set,a target quantity of to-be-recommended sticker sets having highestsimilarities with the historical sticker set may be selected, and then acomprehensive similarity score of each selected to-be-recommendedsticker set is calculated.

For example, assuming that the historical sticker set includeshistorical sticker sets A, B, and C, the server may select, from all therecommendable sticker sets, n sticker sets having highest similaritieswith the historical sticker set A that are respectively A₁, A₂, A₃ . . .A_(n), n sticker sets having highest cosine similarities with thesticker set B that are respectively B₁, B₂, B₃ . . . B_(n), and nsticker sets having highest similarities with the sticker set C that arerespectively C₁, C₂, C₃ . . . C_(n), n is a preset target quantity, anda value of n may be set as required. For example, the value of n may bethe same as the preset quantity or may be greater than the presetquantity. Then, the server may calculate comprehensive similarity scorescorresponding only to these selected sticker sets, obtain arecommendation sequence of the selected to-be-recommended sticker setsaccording to the comprehensive similarity scores of the selectedto-be-recommended sticker sets, and finally select a preset quantity ofto-be-recommended sticker sets that rank high and recommend theto-be-recommended sticker sets to the terminal.

It is noted that, in the embodiment in FIG. 2, step S206 is an exemplaryimplementation of determining an emotion feature of a sticker setaccording to emotion features of stickers in the sticker set. In otherembodiments, there may further be a plurality of manners of determiningthe emotion feature of the sticker set based on the emotion features ofthe stickers in the sticker set. For example, FIG. 5 is a flowchart ofanother implementation of determining an emotion feature of a stickerset. It may be learned from FIG. 5 that, the procedure may include thefollowing steps:

In step S501, a total quantity of sending times of each sticker in thesticker set is obtained.

The total quantity of sending times of the sticker is, for example, atotal sum of quantities of times of sending the sticker by all users ona network.

For example, a sticker H is sent by a user M1 for 10 times, is sent by auser M2 for 20 times, and is sent by a user M3 for 5 times. Therefore, atotal quantity of sending times of the sticker H is 35.

In step S502, a specified quantity of sticker that rank high isselected, according to a descending order of the total quantities ofsending times of the stickers in the sticker set.

The specified quantity may be set as required. For example, thespecified quantity may be 5.

In step S503, weights of the selected stickers are determined accordingto total quantities of sending times of the selected stickers.

Generally, a larger total quantity of sending times of a stickerindicates a correspondingly larger weight of the sticker. This may beset as required.

In step S504, emotion features of the selected stickers are obtained.

For an exemplary manner of obtaining the emotion features of thestickers, refer to the related descriptions in the foregoingembodiments, and details are not described herein again.

In step S505, weighted summation on the emotion features of thespecified quantity of selected stickers is performed according to theweights of the selected stickers, and a result of the weighted summationis used as the emotion feature of the sticker set.

It may be understood that, a sticker having a larger total quantity ofsending times in the sticker set has an emotion feature more concernedby a user. Therefore, the specified quantity of stickers having largesttotal quantities of sending times are selected, and the emotion featureof the sticker set is determined according to the emotion features ofthe selected stickers, helping to more properly determine the emotionfeature of the sticker set.

It is noted that, the procedure of determining the emotion feature ofthe sticker set in FIG. 5 may be applicable to a case in which theserver determines an emotion feature of a sticker set in real time.Alternatively, the server may pre-perform the procedure shown in FIG. 5,to pre-obtain the emotion feature of each sticker set and store theemotion feature in the server or the database.

For ease of understanding the embodiments of this application, thesticker recommendation method in the embodiments of this application isdescribed below with reference to an embodiment. An example in which aninstant messaging server recommends a sticker set to an instantmessaging user in an instant messaging process is used for description.FIG. 6 is a schematic flowchart of a sticker set recommendation methodapplied to an instant messaging embodiment according to thisapplication. The method in this embodiment may include the followingsteps:

In step S601, the instant messaging user logs in to the instantmessaging server by using an instant messaging client.

In step S602, the instant messaging client sends a sticker store entryrequest to the server if detecting that the user taps a sticker storeoption of an instant messaging window, where the sticker store entryrequest carries an identifier of the user.

FIG. 7 is a schematic diagram of a sticker selection interface presentedon an instant messaging window. If the user taps a “stickerrecommendation button” 701 used for triggering sticker recommendation onthe interface, the instant messaging client is triggered to send thesticker store entry request to the server.

It is noted that FIG. 7 is merely an example. In other embodiments,except button tapping, the user may request to enter the sticker storeby inputting a website or in other manners.

It is noted that, in this embodiment, the sticker store entry request isused as an example of a sticker recommendation request for description.However, it may be understood that, in different embodiments, thesticker recommendation request sent by the terminal may be different.

In step S603, the server queries, according to the identifier of theuser in response to the sticker store entry request, a set of historicalstickers sent by the user, where the set of historical stickers includesa plurality of historical stickers.

In step S604, the server determines at least one historical sticker setto which the plurality of historical stickers included in the set ofhistorical stickers belongs.

In step S605, the server determines a plurality of sticker sets in asticker set list other than the historical sticker set as recommendableto-be-recommended sticker sets.

In step S606, the server queries an emotion feature of the historicalsticker set and emotion features of the recommendable sticker setsaccording to emotion features of stored sticker sets.

In step S607, for each historical sticker set, the server calculates asimilarity between each to-be-recommended sticker set and the historicalsticker set according to the emotion feature of each to-be-recommendedsticker set and the emotion feature of the historical sticker set.

In step S608, for each historical sticker set, the server selects atarget quantity of to-be-recommended sticker sets according to adescending sequence of the similarities between the to-be-recommendedsticker sets and the historical sticker set, to obtain a plurality ofselected to-be-recommended sticker sets.

It may be understood that, the target quantity of to-be-recommendedsticker sets may be selected for each historical sticker set, andto-be-recommended sticker sets corresponding to different historicalsticker sets may be partially the same. For example, a sticker set L maybe one of a target quantity of to-be-recommended sticker sets selectedfor a historical sticker set A, and may further be one of a targetquantity of to-be-recommended sticker sets selected for a historicalsticker set B.

In step S609, for a selected to-be-recommended sticker set, the serversums the similarity between the to-be-recommended sticker set and eachhistorical sticker set, and uses a summation result as a comprehensivesimilarity score of the to-be-recommended sticker set.

In step S610, the server determines a recommendation sequence of theplurality of selected to-be-recommended sticker sets according to adescending order of the comprehensive similarity scores.

In step S611, the server determines a preset quantity of target stickersets that rank high in the recommendation sequence of the plurality ofselected to-be-recommended sticker sets.

In step S612, the server sends a recommendation sequence correspondingto the preset quantity of target sticker sets to the terminal.

In step S613, the terminal sequentially exhibits the preset quantity oftarget sticker sets on a sticker store page according to therecommendation sequence of the preset quantity of target sticker sets.

In an embodiment, the recommendation sequence corresponding to thetarget sticker sets that is determined by the server may includeinformation such as identifiers of the target sticker sets and ranks ofthe target sticker sets in the recommendation sequence. Correspondingly,icons of the target sticker sets may be sequentially presented on thesticker store page presented in the terminal. FIG. 8 is a schematicdiagram of a sticker store interface presented in a terminal. It may belearned from FIG. 8 that, there is a sticker recommendation bar in thesticker store, and a plurality of recommended sticker sets, for example,a sticker set A and a sticker set B, is exhibited in the stickerrecommendation bar.

In an embodiment, to properly determine a sticker set having arelatively high degree of similarity with the historical sticker set asa recommendable sticker set and further to reduce a computing amount ofthe server, the server may cluster a plurality of sticker sets accordingto emotion features of the plurality of sticker sets, to cluster stickersets having similar emotion features into one classification. It isnoted that after a plurality of classifications is obtained throughclustering, classification labels may be constructed for theclassifications, to distinguish between the classifications.

Correspondingly, after determining the at least one historical stickerset to which the historical stickers sent by the user belongs, theserver may determine a classification of the historical sticker set inthe plurality of classifications, and then determine the plurality ofrecommendable to-be-recommended sticker sets in the classification ofthe historical sticker set. For example, a plurality of sticker sets inthe classification of the historical sticker set other than thehistorical sticker set is used as the plurality of recommendableto-be-recommended sticker sets.

A sticker set having a same classification as that of the historicalsticker set has an emotion feature whose similarity with the emotionfeature of the historical sticker set is relatively large. Therefore,the plurality of sticker sets in the classification of the historicalsticker set other than the historical sticker set is used as theplurality of recommendable to-be-recommended sticker sets, to decrease aquantity of to-be-recommended sticker sets while ensuring that therecommended sticker sets have relatively high similarities with thehistorical sticker set, thereby reducing a data computing amount needingto be consumed for calculating similarities between to-be-recommendedsticker sets and the historical sticker set and for obtaining therecommendation sequence.

It may be understood that each sticker set in the server may be uploadedby a research and development personnel of the sticker to the server. Asimilarity between a sticker set needing to be uploaded and an existingsticker set may further be calculated based on emotion features of thesticker sets, to assist in review and screen out a plagiarized stickerset.

In an embodiment, each sticker set stored in the server may be obtainedin the following manner:

obtaining a to-be-posted sticker set if receiving a request for postinga sticker set;

extracting an emotion feature of each sticker included in theto-be-posted sticker set;

determining an emotion feature of the to-be-posted sticker set accordingto the emotion feature of the sticker in the to-be-posted sticker set;

calculating, according to emotion features of a plurality of storedsticker sets and the emotion feature of the to-be-posted sticker set, asimilarity between the to-be-posted sticker set and each stored stickerset; and

posting the to-be-posted sticker set if there is no sticker set whosesimilarity with the to-be-posted sticker set is less than a presetthreshold in the plurality of stored sticker sets.

It may be understood that, during recommendation of a sticker set, inthe embodiments of this application, a sticker may further berecommended. Referring to FIG. 9, an sticker recommendation embodimentin this application includes the following steps:

In step S901, a usage record of each group of stickers used by a user isobtained, where each group of stickers corresponds to at least one imagestyle, and each group of stickers includes at least one pictures.

A service platform may preset a classification system of image styles ofstickers. In an embodiment, image styles in a classification system mayinclude: a cute 2D sticker, a funny 2D sticker, a cute 3D sticker, afunny 3D sticker, a real-person sticker, a real-animal sticker, awordart sticker, and the like. Various image styles included in theclassification system are merely used as an example for description. Inanother embodiment, the service platform may set more or fewer imagestyles according to different requirements. Types of the image styles inthe classification system are not limited in this application.

When developing stickers, a third-party developer usually develops thestickers in sets. Each set of stickers usually includes at least twopictures (that is, each set of stickers is equivalent to the foregoingsticker set), and image styles of pictures in the same set of stickersusually remain the same. Therefore, in the present disclosure, each setof stickers may be used as a group of stickers that are of one imagestyle.

In an embodiment, in a process in which the user uses a social networkapplication, the user may create a sticker through doodle, may save asticker posted by another user in the social network application, maycopy a sticker from another social network application and use thesticker in the current social network application, and so on. Thesestickers usually independently exist and do not exist in a set withother stickers. Such stickers may each be separately processed as onegroup.

In an embodiment, each group of stickers may correspond only to oneimage style, or may correspond to two or more image styles. For example,using the classification system as an example, a group of stickers maycorrespond only to the image style of cute 3D sticker, or may correspondto two image styles, namely, the cute 3D sticker and the real-personsticker.

For a group of stickers, when the group of stickers includes only onepicture, an image style of the group of stickers is an image style ofthe picture. When the group of stickers includes at least two pictures,an image style of the group of stickers is an image style of eachpicture in the group of stickers. For example, when a group of stickersincludes 10 pictures, if an image style of the group of stickers is thecute 3D sticker, it indicates that an image style of each of the 10pictures is the cute 3D sticker; or if an image style of the group ofstickers includes two image styles, namely, the cute 3D sticker and thereal-person sticker, it indicates that an image style of each of the 10pictures includes the cute 3D sticker and the real-person sticker.

In an embodiment, the step of obtaining a usage record of each group ofstickers used by a user may be performed by the server 101 shown inFIG. 1. For example, the server 101 may obtain an operation record ofthe user in the social network application, to extract an operationrecord of the user for using the stickers, and generate, throughstatistics, the usage record of each group of stickers used by the user.

In an embodiment, using an example in which the usage record includes aquantity of times of using each group of stickers by the user, theserver 101 collects a sticker used by the user each time, and increasesa quantity of use times corresponding to a group of stickers to whichthe sticker belongs by 1, to finally obtain the quantity of times ofusing each group of stickers by the user.

In an embodiment, the usage record of each group of stickers used by theuser is specifically a usage record of each group of stickers havingbeen used by the user. That is, the usage record obtained in step 901 isa usage record of each group of stickers of the user.

In an embodiment, that the user uses a sticker may be that, the userposts the sticker by using a social application message in the socialnetwork application. The social application message may be an instantmessaging message or a personal dynamic. That is, that the user uses asticker may be that, the user posts the sticker by using an instantmessaging window, a group chat window, or the personal dynamic.

In an embodiment, in the process in which the user posts a sticker byusing a social application message, the user may post two or more samestickers in the same social application message (where for example, theuser posts a plurality of totally same stickers in a message or apersonal dynamic). To avoid interference of this case and accuratelycollect a frequency of using each group of stickers by the user, whenthe server 101 collects the sticker used by the user each time, for aplurality of same stickers posted in the same social applicationmessage, a quantity of use times corresponding to a group of stickers towhich the sticker belongs is increased by 1 corresponding to the socialapplication message.

For example, using an example in which a group of stickers includes asticker 1 and a sticker 2, when the user posts a personal dynamic, threestickers 1 and two stickers 2 are posted in one personal dynamic.Therefore, when collecting the usage record of the sticker used by theuser, the server 101 increases a quantity of use times corresponding tothe group of sticker by 1 for the sticker 1 in the personal dynamic, andincreases the quantity of use times corresponding to the group ofstickers by 1 for the sticker 2 in the personal dynamic.

In an embodiment, when the user posts two or more same stickers in thesame social application message, it may be considered that the userfavors the sticker. Therefore, when the server 101 collects the stickerused by the user each time, for a plurality of same stickers posted inthe same social application message, the server 101 adds, correspondingto the social application message, a quantity of stickers in the socialapplication message to a quantity of use times corresponding to a groupof stickers to which the sticker belongs.

For example, using an example in which a group of stickers includes asticker 1 and a sticker 2, when the user posts a personal dynamic, threestickers 1 and two stickers 2 are posted in one personal dynamic.Therefore, when collecting the usage record of the sticker used by theuser, the server 101 increases a quantity of use times corresponding tothe group of stickers by 3 for the sticker 1 in the personal dynamic,and increases the quantity of use times corresponding to the group ofstickers by 2 for the sticker 2 in the personal dynamic.

In an embodiment, the user may favor an image style of a sticker in aperiod. For example, the user uses the cute 2D sticker for a relativelylarge quantity of times once in a period of time, and may prefer to usethe real-person sticker in another period of time. Therefore, to improverecommendation accuracy, when obtaining the usage record of each groupof stickers used by the user, the server 101 may obtain a usage recordof each group of stickers used by the user in a preset-length timeperiod closest to a current time, so that a subsequent recommendationresult can be closer to a preference of the user in the last period oftime. A time length of the preset-length time period may be manually setby a manager, or may be a fixed time length set in the server 101 bydefault. For example, assuming that the fixed time length is one month,when obtaining the usage record of each group of stickers used by theuser, the server 101 may obtain a usage record of each group of stickersthat is within the last month closest to the current time.

In step S902, an uncorrected recommendation index of a specified stickeris obtained, and an image style of the specified sticker is obtained.

The recommendation index may be used for indicating a priority ofrecommending the specified sticker to the user. For example, a largervalue of the recommendation index indicates that the specified stickeris easier to be accepted by the user when the specified sticker isrecommended to the user.

In an embodiment, a server cluster may calculate the uncorrectedrecommendation index of the specified sticker according to acollaborative-filtering-based recommendation algorithm.

In an embodiment, step S902 may be performed by the server 101 in theservice platform 10 shown in FIG. 1. The collaborative-filtering-basedrecommendation algorithm is a general term of a type of recommendationalgorithms. A core idea of the collaborative-filtering-basedrecommendation algorithm is: A user-item matrix, that is, a user-item(where the item is each group of stickers in this application) scorematrix, is first constructed, then a user-item matrix of another userhaving a similar preference with a current user (that is, another userfavoring the same sticker as that of the current user) is found by usingthe collaborative-filtering-based recommendation algorithm according tothe constructed user-item matrix, and a sticker that may be liked by theuser is predicted according to the user-item matrix of the another userhaving the similar preference with the current user.

In an embodiment, it is assumed that a higher frequency of using a groupof stickers by the user indicates a higher preference of the user forthe group of stickers. That is, the frequency of using each group ofstickers by the user is used for replacing a user score in a generalmeaning. The server 101 constructs, according to the frequency of usingeach group of stickers by each user, a user-item matrix corresponding toeach user. In a user-item matrix corresponding to a user, each elementcorresponds to a frequency of using one group of stickers by the user.Different users have different use habits, and in the constructeduser-item matrices corresponding to different users, original valuesindicating frequencies of using each group of stickers by the usersgreatly differ from each other. Therefore, to reduce complexity in asubsequent computing process, before further processing, the server 101normalizes the constructed user-item matrix of each user. Thenormalization herein means separately normalizing the user-item matrixcorresponding to each user. That is, for each user, a value of eachelement in the user-item matrix of the user is equal to a value obtainedby dividing a frequency of using a group of stickers corresponding tothe element by the user by a maximum value in a frequency of using eachgroup of stickers by the user. A formula is as follows:

${{uimatrix}\left\lbrack {u,e} \right\rbrack} = \frac{f\left( {u,e} \right)}{\max_{e = 1}^{K}\left( {f\left( {u,e} \right)} \right)}$

f(u, e) is a frequency of using a group e of stickers by a user u, and Kis a quantity of groups of all stickers. uimatrix[u,e] is a valueobtained after normalization of an element corresponding to the group eof stickers in a user-item matrix corresponding to the user. Each groupof stickers not used by the user (that is, each group of stickerscorresponding to f(u, e)=0) is stickers needing to be predictivelyscored (that is, to obtain the recommendation index) by using therecommendation algorithm.

After the user-item matrix of each user is normalized, the server 101finds out, by using the collaborative-filtering-based recommendationalgorithm based on the user-item matrix of each user, the user-itemmatrix of the another user having the similar preference with thecurrent user, and calculates a predictor of an element being 0 in theuser-item matrix of the current user with reference to the user-itemmatrix of the another user having the similar preference with thecurrent user. A sticker corresponding to the element being 0 in theuser-item matrix of the current user may be the specified sticker. Therecommendation index of the specified sticker may be obtained by usingthe predictor of the element. For example, the predictor of the elementmay be directly used as the recommendation index of the specifiedsticker. That is, for each user, a recommendation index of each group ofstickers not used by the user is calculated and used as a recommendationbasis.

In an embodiment, the example in which the uncorrected recommendationindex is obtained based on the collaborative-filtering-basedrecommendation algorithm is used for description in the foregoingsolution. In an embodiment, the server cluster may alternatively obtainthe uncorrected recommendation index by using another type ofrecommendation algorithm according to a requirement of the embodiment.For example, another type of recommendation algorithm may include acontent-based recommendation algorithm, an association-rule-basedrecommendation algorithm, a knowledge-based recommendation algorithm,and the like. Even more, the server cluster may alternatively obtain theuncorrected recommendation index according to a combination of two ormore recommendation algorithms. The recommendation algorithm used by theservice platform for obtaining the uncorrected recommendation index isnot limited in this application.

In an embodiment, in another possible implementation, the serviceplatform may not obtain the uncorrected recommendation index by using aspecific recommendation algorithm. For example, the service platform mayset the uncorrected recommendation index of the specified sticker to afixed value, and uncorrected recommendation indexes of allto-be-recommended stickers are the same. That is, the service platformrecommends a sticker to the user according only to the user record ofeach group of stickers used by the user but not with reference toanother recommendation algorithm.

S903. Generate an interest vector of the user according to the usagerecord of each group of stickers used by the user and the image style ofeach group of stickers.

Each element in the interest vector indicates a quantity of times ofusing a sticker that is of one image style by the user.

In an embodiment, when the usage record includes a quantity of times ofusing each group of stickers by the user, a process of generating theinterest vector of the user may be described in the following step a tostep c.

Step a. Generate an initialized interest vector according to a quantityof image styles, where a value of an element corresponding to each imagestyle in the initialized interest vector is 1.

When using stickers, generally, the user uses stickers of only severalimage styles, and may never use stickers of most image styles. Toprevent the vector from being excessively sparse due to excessive valuesbeing 0 in the interest vector of the user and to improve accuracy ofsubsequent calculation, in the method described in the presentdisclosure, it may be considered by default that the user uses a stickerof each image style at least once. That is, the initialized interestvector is an all-ones vector v=[1, 1, . . . , 1], a quantity of elementsin the vector v is a quantity (assuming to be n) of image styles in theclassification system of the stickers, and each element in the vector vcorresponds to one image style.

Step b. For each group of stickers, add a value of an elementcorresponding to an image style of the group of stickers in theinitialized interest vector to a quantity of times of using the group ofstickers by the user, to obtain a vector after the addition.

The server cluster may collect all the stickers used by the user andfrequencies of the stickers according to the usage record. For eachgroup of stickers and a corresponding use frequency m, the element, forexample, an i^(th) element, corresponding the image style correspondingto the group of stickers in the vector v is determined, and then m isadded to the i^(th) element in the vector v, that is:

For example, assuming that the first element in the vector v correspondsto the cute 2D sticker, the second element corresponds to the funny 2Dsticker, the last element corresponds to the wordart sticker, and it isdetermined through statistics according to the usage record that aquantity of times of using the cute 2D sticker by the user is 200, aquantity of times of using the funny 2D sticker is 0, and a quantity oftimes of using the wordart sticker is 30, the vector obtained throughaddition is v=[201, 1, . . . , 31].

In an embodiment, when one group of stickers corresponds to two or moreimage styles, each time the user uses a usage record of the group ofstickers, quantities of times of using stickers of the two or more imagestyles by the user are respectively increased by 1.

For example, a group of stickers corresponds to the cute 2D sticker andthe real-person sticker, and a quantity of times of using the group ofstickers by the user is 10. Therefore, when quantities of times of usingthe cute 2D sticker and the real-person sticker by the user arecollected, the quantities of times of using the cute 2D sticker and thereal-person sticker by the user are respectively increased by 10according to a usage record of the group of stickers used by the user.

Step c. Normalize the vector obtained through addition, to obtain theinterest vector of the user.

When normalizing the vector obtained through addition, the servercluster may divide all elements in the vector v by a length of thevector v, so that a length of the interest vector of the user that isobtained after the normalization is 1. A normalization formula is asfollows:

${v\lbrack i\rbrack} = \frac{v\lbrack i\rbrack}{\sqrt{\sum_{j = 1}^{n}{v\lbrack j\rbrack}^{2}}}$

v[i] is an i^(th) element in the vector v, v[j] is a j^(th) element inthe vector v, and n is the length of the vector v.

S904. Correct the recommendation index of the specified stickeraccording to the interest vector, the image style of the specifiedsticker, and a preset correction formula, to obtain a correctedrecommendation index.

In an embodiment, the correction formula is:

${{rp}\left( {u,e} \right)} = {{{cf}\left( {u,e} \right)}*\left( {1 + {\left( {\frac{2}{1 + {\exp \left( {{- 0.1}*{{frq}(u)}} \right)}} - 1} \right)*{{var}\left( {v(u)} \right)}*{{sim}\left( {e,{v(u)}} \right)}}} \right)}$

rp(u,e) is the corrected recommendation index, cf(u,e) is theuncorrected recommendation index, and v(u) is the interest vector of theuser.

In addition, frq(u) is an average quantity of times of using thestickers by the user per unit of time period. The unit of time periodmay be a preset time period having fixed duration. For example, the unitof time period may be one day or an hour. The average quantity of timesof using the stickers by the user per unit of time period is a quantityof use times obtained by averaging a collected total quantity of timesof using each group of stickers by the user in the usage record to eachunit of time period corresponding to the usage record. For example,assuming that the usage record is a usage record of the user within 30days, the unit of time period is one day, and the collected totalquantity of times of using the stickers by the user in the usage recordis 10000, the average quantity of times of using the stickers by theuser per unit of time period is 10000/30≈333 times.

$\frac{2}{1 + {\exp \left( {{- 0.1}*{{frq}(u)}} \right)}} - 1$

in the foregoing formula is a function related to the average quantityof times of using the stickers by the user per unit of time period.

FIG. 10 is a schematic diagram of a function graph according to anembodiment of the present disclosure. As shown in FIG. 10, the functionrelated to the average quantity of times of using the stickers by theuser per unit of time period is half an s-shaped function. When anaverage use frequency of the user per unit of time period is 0, a valueof the function is 0, indicating that reliability of correcting therecommendation index by using the interest vector of the user is 0 andthe interest vector is completely unavailable. As the average usefrequency of the user per unit of time period increases, the functionfirst increases and then gradually becomes gentle, and has a limit valueof 1.0. This change trend indicates that as the average frequency ofusing the stickers by the user per unit of time period increases,correcting the recommendation index by using the interest vector of theuser becomes more reliable.

var(v(u)) in the foregoing formula is a variance value of each elementin the interest vector of the user. A larger variance value indicatesmore centralized image styles of the stickers used by the user.Otherwise, a smaller variance value indicates more dispersed imagestyles of the stickers used by the user. If the image styles of thestickers used by the user are more centralized, an amplitude ofcorrecting the recommendation index by using the formula is larger;otherwise, an amplitude of correcting the recommendation index by usingthe formula is smaller.

sim(e,v(u)) in the foregoing formula is a cosine similarity between thespecified sticker and the interest vector.

When calculating the cosine similarity, the server cluster may determinean image style vector corresponding to the specified sticker. The imagestyle vector is similar to the interest vector of the user and alsoincludes n elements. Each element corresponds to a image style, a valueof an element corresponding to the image style of the specified stickeris 1, and other elements are all 0. The server cluster selects elementsnot being 0 at corresponding locations in both the image style vectorand the interest vector of the user, multiplies and sums the elements toobtain a value, and divides the value by lengths of the two vectors. Anobtained result is the cosine similarity.

In an embodiment, when the specified sticker corresponds only to oneimage style, the interest vector v(u) of the user is normalized, and invector space of the same dimensions, a value of only one element(assuming to be an i^(th) element) corresponding to the specifiedsticker is 1 and other values are all 0. In this case, there is only onegroup of elements not being 0 at corresponding locations in both theimage style vector and the interest vector of the user, and the lengthsof the two vectors are both 1. Therefore, according to the foregoingcosine similarity calculation formula, the value of the i^(th) elementin the interest vector of the user is the cosine similarity. That is,when the specified sticker corresponds only to one image style, acalculation method for the cosine similarity is: first determining anelement (assuming to be an i^(th) element) corresponding to the imagestyle of the specified sticker in the interest vector v(u) of the user,and then determining a value of sim(e,v(u)) as a value of the i^(th)element of the vector v(u).

S905. Recommend the specified sticker to the user when the correctedrecommendation index satisfies a recommendation condition.

The recommendation condition may include at least one of the followingconditions:

the corrected recommendation index is greater than a preset indexthreshold, and a rank of the corrected recommendation index inrecommendation indexes of to-be-recommended stickers is greater than apreset rank threshold.

After correcting the recommendation index of the specified sticker to berecommended to the user, the service platform may determine, accordingonly to the corrected recommendation index of the specified sticker,whether to recommend the specified sticker. For example, the serviceplatform may preset the index threshold (where a manager may manuallyset a value), and may recommend the specified sticker to the user whenthe corrected recommendation index is higher than the index threshold.

Alternatively, the service platform may determine, with reference to arecommendation index of another to-be-recommended sticker, whether torecommend the specified sticker. For example, the service platform maysort the to-be-recommended stickers according to correctedrecommendation indexes corresponding to the to-be-recommended stickers,and recommend stickers at first x ranks to the user. If the specifiedsticker is in the first x ranks, the service platform recommends thespecified sticker to the user. x is the rank threshold, and may be avalue set by the manager in the service platform or may be a value setby default in the service platform.

Alternatively, the service platform may determine, with reference to arank of the recommendation index and a specific value of therecommendation index, whether to recommend the specified sticker. Forexample, the service platform may sort the to-be-recommended stickersaccording to corrected recommendation index of the to-be-recommendedstickers. If the specified sticker is in the first x ranks and thecorrected recommendation index of the specified sticker is greater thanthe index threshold, the service platform recommends the specifiedsticker to the user. Otherwise, if the specified sticker is out of thefirst x ranks or the corrected recommendation index of the specifiedsticker is not greater than the index threshold, the service platformdoes not recommend the specified sticker to the user.

In an embodiment, the specified sticker may be a group of pictures inthe stickers to be recommended to the user, which means that after thestickers are classified, a recommendation index of a group of stickersmay be corrected by using the method described in the presentdisclosure. Alternatively, the specified sticker may be one of severalgroups of stickers having highest corresponding uncorrectedrecommendation indexes in the stickers to be recommended to the user.

In an embodiment, the service platform may regularly perform step S901to step S905, for example, perform the foregoing solution by using halfa month or one month as a period, to regularly update the stickers to berecommended to the user.

In conclusion, according to an embodiment of the sticker recommendationmethod provided in the present disclosure, after the recommendationindex of the specified sticker is corrected by using the usage record ofeach group of stickers used by the user, the image style of each groupof stickers, and the image style of the specified sticker, the stickerrecommended to the user according to the corrected recommendation indexis a sticker obtained by comprehensively considering a preference of theuser for the image style of the sticker, to implement personalizedrecommendation of the sticker to the user with reference to the personalpreference of the user for the image style of the sticker, therebyimproving an effect of recommending a sticker to a single user.

In addition, according to an embodiment of the method provided in thepresent disclosure, the uncorrected recommendation index is calculatedby using the collaborative-filtering-based recommendation algorithm, andthe uncorrected recommendation index is corrected with reference to thepersonal preference of the user for the image style of the sticker, torecommend the sticker to the user with reference to the personalpreference of the user and the collaborative-filtering-basedrecommendation algorithm.

In addition, according to n embodiment of the method provided in thepresent disclosure, during generation of the interest vector of theuser, the initialized interest vector in which the value of the elementcorresponding to each image style is 1 is first generated, and thequantity of times of using the sticker of each image style is added tothe initialized interest vector, to obtain the vector through additionand perform normalization processing, to prevent the vector from beingexcessively sparse due to excessive values of 0 in the interest vectorof the user, thereby improving the accuracy of the subsequentcalculation.

In addition, according to n embodiment of the method provided in thepresent disclosure, when the recommendation index of the specifiedsticker is corrected, impact of the average quantity of times of usingthe stickers by the user per unit of time period and interestcentralization of the user on the correction is comprehensivelyconsidered, thereby improving accuracy of correction of therecommendation index.

FIG. 11 is a flowchart of a sticker recommendation method according toan embodiment. Using an example in which the sticker recommendationmethod is applied to the service platform 10 in the system shown in FIG.1, the sticker recommendation method may include the following severalsteps:

In S1101, sample stickers corresponding to each image style areobtained, where the sample stickers are some stickers whosecorresponding image styles are specified in a sticker library.

The sticker library includes each group of stickers used by a user and aspecified sticker. For example, using the system shown in FIG. 1 as anexample, the sticker library may be set in the server 101 or thedatabase 102, and the sticker library stores each group of stickersexisting in the system. For a single user, in addition to each group ofstickers used by the user, the sticker library further stores aspecified sticker that is not used by the user and that may berecommended to the user.

In an embodiment, the sample stickers may be manually labeled by amanager. A server cluster obtains the sample stickers that are manuallylabeled and that correspond to each image style.

The sample stickers may be labeled in groups. For example, afterconstructing a classification system of image styles of stickers, themanager may first label, in all groups of stickers in the stickerlibrary, at least one group of stickers corresponding to each imagestyle. A quantity of groups of stickers labeled for each image style maybe determined by the manager according to an actual case. For example,comprehensively considering labor costs and a subsequent machinelearning effect, in a classification system having normal difficulty, 50groups of stickers may be approximately labeled for each image style.

In step S1102, image feature information of the sample stickerscorresponding to each image style is extracted.

In an embodiment, the server cluster may perform image classification byusing a machine-learning classification model suitable for imageclassification.

Currently, machine-learning classification models suitable for imageclassification are mainly classified into two types. One type is aconventional machine-learning classification model, for example, asupport vector machine (SVM) model, a maximum-entropy model, or a randomforest model. The other type is a deep neural network model, forexample, a convolutional neural network model. Manners required by thetwo types of machine-learning classification models for extracting imagefeature information are also different.

In an embodiment, if the conventional machine-learning classificationmodel is used, the server cluster may extract the image featureinformation from the sample stickers corresponding to each image styleby using an image feature extraction algorithm such as a scale-invariantfeature transform (SIFT), speed-up robust features (SURF), oriented fastand rotated brief (ORB), a histogram of oriented gradient (HOG), or alocal binary pattern (LBP).

If the deep neural network model is used, the server cluster may extracta red green blue (RGB) color value of each pixel point from the samplestickers corresponding to each image style as the image featureinformation.

In step S1103, machine-learning training on the image featureinformation and the image style corresponding to the image featureinformation is performed, to obtain a machine-learning classificationmodel.

After extracting the image feature information of the sample stickerscorresponding to each image style in the foregoing step, the servercluster may input the image feature information and the image stylecorresponding to the image feature information into a selected machinemodel for training, to obtain the machine-learning classification modelused for sticker classification.

In step S1104, image feature information of an unclassified sticker isinput into the machine-learning classification model, to obtain an imagestyle corresponding to the unclassified sticker.

After the machine-learning classification model is trained, for each ofother unclassified groups of stickers (that is, groups of stickers whoseimage styles are not labeled by the manager), the server cluster mayextract image feature information of each group of stickers according toa corresponding machine-learning classification model, and input theimage feature information of each group of stickers into themachine-learning classification model. Then, the machine-learningclassification model may output an image style corresponding to eachgroup of stickers.

In step S1105, a usage record of each group of stickers used by a useris obtained, where each group of stickers corresponds to at least oneimage style, and each group of stickers includes at least one pictures.

In step S1106, an uncorrected recommendation index of a specifiedsticker is obtained, and an image style of the specified sticker isobtained.

In step S1107, an interest vector of the user is generated according tothe usage record of each group of stickers used by the user and theimage style of each group of stickers.

In step S1108, the recommendation index of the specified sticker iscorrected according to the interest vector, the image style of thespecified sticker, and a preset correction formula, to obtain acorrected recommendation index.

In step S1109, the specified sticker is recommended to the user when thecorrected recommendation index satisfies a recommendation condition.

For execution processes of step S1105 to step S1109, refer to thedescriptions of step 5901 to step S905 in the embodiment shown in FIG.9, for example, and details are not described herein again.

In conclusion, according to an embodiment of the sticker recommendationmethod provided in the present disclosure, after the recommendationindex of the specified sticker is corrected by using the usage record ofeach group of stickers used by the user, the image style of each groupof stickers, and the image style of the specified sticker, the stickerrecommended to the user according to the corrected recommendation indexis a sticker obtained by comprehensively considering a preference of theuser for the image style of the sticker, to implement personalizedrecommendation of the sticker to the user with reference to the personalpreference of the user for the image style of the sticker, therebyimproving an effect of recommending a sticker to a single user.

In addition, according to an embodiment of the method provided in thepresent disclosure, the image feature information of the sample stickerscorresponding to each image style is obtained; machine-learning trainingis performed on the image feature information and the image stylecorresponding to the image feature information, to obtain themachine-learning classification model; and the image feature informationof the unclassified sticker is input into the machine-learningclassification model, to obtain the image style corresponding to theunclassified sticker, thereby implementing automatic classification ofthe image style of each group of stickers.

FIG. 12 is a schematic diagram of an implementation process ofrecommending a sticker to a user by a server cluster. An example inwhich the implementation process may be implemented by the server 101 inthe server cluster shown in FIG. 1 is used. As shown in FIG. 12, amanager labels image styles of some stickers in a sticker library of theserver 101 or the database 102. 50 groups of stickers are approximatelylabeled for each image style. The server 101 obtains the stickers whoseimage styles are labeled by the manager as sample stickers correspondingto each image style, extracts image feature information of the samplestickers, and performs machine learning on the extracted image featureinformation of the sample stickers and the image style (that is, theimage style labeled by the user) corresponding to the image featureinformation, to obtain a machine-learning classification model. Theserver 101 extracts, by using the same method, image feature informationof each of other groups of stickers whose image styles are not labeledin the sticker library, inputs the image feature information of eachgroup of stickers into the machine-learning classification model, andoutputs an image style corresponding to each group of stickers, toobtain the image style corresponding to each group of stickers managedin the server 101.

On the other hand, that a user A uses a social network application byusing the terminal 11 includes using a sticker in the social networkapplication. The server 101 collects an operation record of the user A.The server 101 generates, according to the operation record of the userA at an interval of a preset period, for example, at an interval of onemonth, a user record of each group of stickers used by the user A withinthe last month. The usage record includes a quantity of times of usingeach group of stickers by the user A.

After obtaining the usage record of each group of stickers used by theuser A in the last month, the server 101 generates an interest vector ofthe user A with reference to the usage record and the image style ofeach group of stickers. In addition, the server 101 further calculates,by using a collaborative-filtering-based recommendation algorithmaccording to a usage record of each group of stickers used by each userA within the last month, an uncorrected recommendation index of eachgroup of stickers that is to be recommended to the user A and that isnot used by the user A. The server 101 corrects the uncorrectedrecommendation index by using the interest vector of the user A, sorts,according to a corrected recommendation index, each group of stickersnot used by the user A, and recommends, to the user A according to asorting result, several groups of stickers having highest recommendationindexes.

A sticker recommendation apparatus in this application is describedbelow.

FIG. 13 is a schematic structural composition diagram of an embodimentof a sticker recommendation apparatus according to this application. Theapparatus in this embodiment may include:

a history query unit 1301, configured to receive a stickerrecommendation request sent by a user by using a terminal, and determineat least one historical sticker set to which a historical sticker sentby the user belongs;

a first feature obtaining unit 1302, configured to obtain an emotionfeature of the historical sticker set;

a second feature obtaining unit 1303, configured to obtain emotionfeatures of a plurality of recommendable to-be-recommended sticker sets;

a similarity calculation unit 1304, configured to: for a historicalsticker set, calculate similarities between the to-be-recommendedsticker sets and the historical sticker set according to the emotionfeatures of the to-be-recommended sticker sets and the emotion featureof the historical sticker set;

a sequence determining unit 1305, configured to determine arecommendation sequence of the plurality of to-be-recommended stickersets according to the similarities between the to-be-recommended stickersets and the historical sticker set; and

a sticker recommendation unit 1306, configured to recommend theto-be-recommended sticker sets to the terminal based on therecommendation sequence.

In an embodiment, an emotion feature of a sticker set that is obtainedby the first feature obtaining unit or the second feature obtaining unitis determined according to an emotion feature of a sticker in thesticker set, the emotion feature of the sticker is a feature extractedfrom the sticker for reflecting an emotion status presented by thesticker, and the sticker set is the historical sticker set or theto-be-recommended sticker set.

In an embodiment, the emotion feature of the sticker set in the firstfeature unit or the second feature unit is obtained in the followingmanner:

obtaining an emotion feature of each sticker in the sticker set; and

calculating an average value of the emotion features of all the stickersin the sticker set, and using the calculated average value as theemotion feature of the sticker set.

In an embodiment, the first feature obtaining unit is configured toobtain the emotion feature of the historical sticker set from emotionfeatures of a plurality of stored sticker sets.

The second feature obtaining unit is configured to obtain, from theemotion features of the plurality of stored sticker sets, the emotionfeatures of the plurality of recommendable to-be-recommended stickersets.

In an embodiment, the sequence determining unit includes:

a comprehensive score subunit, configured to: for a to-be-recommendedsticker set, calculate a comprehensive similarity score of theto-be-recommended sticker set relative to the at least one historicalsticker set according to the similarity between the to-be-recommendedsticker set and each historical sticker set; and

a sequence determining subunit, configured to determine therecommendation sequence of the plurality of to-be-recommended stickersets according to a descending order of the comprehensive similarityscores.

In an embodiment, when calculating the comprehensive similarity score ofthe to-be-recommended sticker set relative to the at least onehistorical sticker set according to the similarity between theto-be-recommended sticker set and each historical sticker set, thecomprehensive score subunit is configured to sum the similarity betweenthe to-be-recommended sticker set and each historical sticker set, anduse a summation result as the comprehensive similarity score of theto-be-recommended sticker set relative to the at least one sticker set.

In an embodiment, the sticker recommendation unit includes:

a recommendation selection subunit, configured to select, from theplurality of to-be-recommended sticker sets based on the recommendationsequence, a preset quantity of target sticker sets that rank high; and

a sequence sending subunit, configured to send a recommendation sequencecorresponding to the preset quantity of target sticker sets to theterminal.

In an embodiment, the apparatus may further include:

a sticker classification determining unit, configured to determine,after the history query unit determines the at least one historicalsticker set to which the historical sticker sent by the user belongs, aclassification to which the historical sticker set belongs in aplurality of classifications, where the plurality of classifications isobtained through clustering of sticker sets according to emotionfeatures of the sticker sets in a server; and

a to-be-recommended sticker set determining unit, configured todetermine the plurality of recommendable to-be-recommended sticker setsin the classification to which the historical sticker set belongs.

In an embodiment, the apparatus may further include:

a sticker set obtaining unit, configured to obtain a to-be-postedsticker set if a request for posting a sticker set is received;

an emotion feature extraction unit, configured to extract an emotionfeature of each sticker included in the to-be-posted sticker set;

a set feature determining unit, configured to determine an emotionfeature of the to-be-posted sticker set according to the emotion featureof the sticker in the to-be-posted sticker set;

a set comparison unit, configured to calculate, according to emotionfeatures of a plurality of stored sticker sets and the emotion featureof the to-be-posted sticker set, a similarity between the to-be-postedsticker set and each stored sticker set; and

a set post control unit, configured to post the to-be-posted sticker setif there is no sticker set whose similarity with the to-be-postedsticker set is less than a preset threshold in the plurality of storedsticker sets.

An embodiment of this application further provides a server. The servermay include a sticker recommendation apparatus described above.

FIG. 14 is a structural block diagram of hardware of a server. Referringto FIG. 14, a server 1400 may include: a processor 1401, acommunications interface 1402, a memory 1403, and a communications bus1404.

The processor 1401, the communications interface 1402, and the memory1403 communicate with each other via the communications bus 1404.

In an embodiment, the communications interface 1402 may be an interfaceof a communications module, for example, an interface of a GSM module.

The processor 1401 is configured to execute a program.

The memory 1403 is configured to store the program.

The program may include program code, and the program code includes acomputer-executable instruction.

The processor 1401 may be a central processing unit (CPU) or anapplication-specific integrated circuit (ASIC), or may be configured asone or more integrated circuits for implementing the embodiments of thepresent application.

The memory 1403 may include a high-speed RAM memory, and may furtherinclude a non-volatile memory, for example, at least one magnetic diskmemory.

The program may be used for:

determining, if receiving a sticker recommendation request sent by auser login to a server by using a terminal, at least one historicalsticker set to which a historical sticker sent by the user belongs;

obtaining an emotion feature of the historical sticker set;

obtaining emotion features of a plurality of recommendableto-be-recommended sticker sets;

for a historical sticker set, calculating similarities between theto-be-recommended sticker sets and the historical sticker set accordingto the emotion features of the to-be-recommended sticker sets and theemotion feature of the historical sticker set;

determining a recommendation sequence of the plurality ofto-be-recommended sticker sets according to the similarities between theto-be-recommended sticker sets and the historical sticker set; and

recommending the to-be-recommended sticker sets to the terminal based onthe recommendation sequence.

Based on the sticker recommendation methods shown in FIG. 9 to FIG. 12,FIG. 15 is a schematic structural composition diagram of anotherembodiment of a sticker recommendation apparatus according toembodiments of this application. The sticker recommendation apparatus inthis embodiment may include:

a record obtaining module 1501, a recommendation index obtaining module1502, a correction module 1503, and a recommendation module 1504.

The record obtaining module 1501 is configured to obtain a usage recordof each group of stickers used by a user, where each group of stickerscorresponds to at least one image style, and each group of stickersincludes at least one pictures.

For a specific step performed by the record obtaining module 1501, referto the descriptions of step S901 in FIG. 9, and details are notdescribed herein again.

The recommendation index obtaining module 1502 is configured to obtainan uncorrected recommendation index of a specified sticker, and obtainan image style of the specified sticker, where the recommendation indexis used for indicating a priority of recommending the specified stickerto the user.

The correction module 1503 is configured to correct the uncorrectedrecommendation index according to the usage record, the image style ofeach group of stickers, and the image style of the specified sticker, toobtain a corrected recommendation index.

The recommendation module 1504 is configured to recommend the specifiedsticker to the user when the corrected recommendation index satisfies arecommendation condition.

For a specific step performed by the recommendation module 1504, referto the descriptions of step S905 in FIG. 9, and details are notdescribed herein again.

In an embodiment, the correction module 1503 includes a vectorgeneration unit and a correction unit.

The vector generation unit is configured to generate an interest vectorof the user according to the usage record of each group of stickers andthe image style of each group of stickers, where each element in theinterest vector indicates a quantity of times of using stickers of oneimage style by the user.

The correction unit is configured to correct the recommendation index ofthe specified sticker according to the interest vector, the image styleof the specified sticker, and a preset correction formula.

For a specific step performed by the correction unit, refer to thedescriptions of step S904 in FIG. 9, and details are not describedherein again.

In an embodiment, the usage record includes a quantity of times of usingeach group of stickers by the user, and the vector generation unitincludes a generation subunit, an addition subunit, and a normalizationsubunit.

The generation subunit is configured to generate an initialized interestvector according to a quantity of image styles, where a value of anelement corresponding to each image style in the initialized interestvector is 1.

The addition subunit is configured to: for each group of stickers, add avalue of an element corresponding to an image style of the group ofstickers in the initialized interest vector to a quantity of times ofusing the group of stickers by the user, to obtain a vector after theaddition.

The normalization subunit is configured to normalize the vector obtainedthrough addition, to obtain the interest vector of the user.

For a specific step performed by the vector generation unit, refer tothe descriptions of step S903 in FIG. 9, and details are not describedherein again.

In an embodiment, the correction formula is:

${{{rp}\left( {u,e} \right)} = {{{cf}\left( {u,e} \right)}*\left( {1 + {\left( {\frac{2}{1 + {\exp \left( {{- 0.1}*{{frq}(u)}} \right)}} - 1} \right)*{{var}\left( {v(u)} \right)}*{{sim}\left( {e,{v(u)}} \right)}}} \right)}},$

where

rp(u,e) is the corrected recommendation index, cf(u,e) is theuncorrected recommendation index, frq(u) is an average quantity of timesof using the sticker by the user per unit of time period, v(u) is theinterest vector, var(v(u)) is a variance value of each element in theinterest vector, and sim(e,v(u)) is a cosine similarity between thespecified sticker and the interest vector.

In an embodiment, the recommendation index obtaining module 1502 isconfigured to calculate the uncorrected recommendation index of thespecified sticker according to a collaborative-filtering-basedrecommendation algorithm.

For a specific step performed by the recommendation index obtainingmodule 1502, refer to the descriptions of step S902 in FIG. 9, anddetails are not described herein again.

In an embodiment, the recommendation condition includes at least one ofthe following conditions:

the corrected recommendation index is greater than a preset indexthreshold; and a rank of the corrected recommendation index inrecommendation indexes of to-be-recommended stickers is greater than apreset rank threshold.

In an embodiment, the apparatus further includes: a sample obtainingmodule, a feature extraction module, a training module, and an imagestyle obtaining module.

The sample obtaining module is configured to obtain, before the recordobtaining module obtains the usage record of each group of stickers usedby the user, sample stickers corresponding to each image style, wherethe sample stickers are some stickers whose corresponding image stylesare specified in a sticker library, and the sticker library includeseach group of stickers used by the user and the specified sticker.

For a specific step performed by the sample obtaining module, refer tothe descriptions of step S1101 in FIG. 11, and details are not describedherein again.

The feature extraction module is configured to extract image featureinformation of the sample stickers corresponding to each image style.

For a specific step performed by the feature extraction module, refer tothe descriptions of step S1102 in FIG. 11, and details are not describedherein again.

The training module is configured to perform machine-learning trainingon the image feature information and the image style corresponding tothe image feature information, to obtain a machine-learningclassification model.

For a specific step performed by the training module, refer to thedescriptions of step S1103 in FIG. 11, and details are not describedherein again.

The image style obtaining module is configured to input image featureinformation of an unclassified sticker into the machine-learningclassification model, to obtain an image style corresponding to theunclassified sticker, where the unclassified sticker is a sticker in thesticker library other than the sample stickers.

For a specific step performed by the image style obtaining module, referto the descriptions of step S1104 in FIG. 11, and details are notdescribed herein again.

In conclusion, according to an embodiment of the sticker recommendationapparatus provided in the present disclosure, after the recommendationindex of the specified sticker is corrected by using the usage record ofeach group of stickers used by the user, the image style of each groupof stickers, and the image style of the specified sticker, the stickerrecommended to the user according to the corrected recommendation indexis a sticker obtained by comprehensively considering a preference of theuser for the image style of the sticker, to implement personalizedrecommendation of the sticker to the user with reference to the personalpreference of the user for the image style of the sticker, therebyimproving an effect of recommending a sticker to a single user.

In addition, according to an embodiment of the apparatus provided in thepresent disclosure, the uncorrected recommendation index is calculatedby using the collaborative-filtering-based recommendation algorithm, andthe uncorrected recommendation index is corrected with reference to thepersonal preference of the user for the image style of the sticker, torecommend the sticker to the user with reference to the personalpreference of the user and the collaborative-filtering-basedrecommendation algorithm.

In addition, according to an embodiment of the apparatus provided in thepresent disclosure, during generation of the interest vector of theuser, the initialized interest vector in which the value of the elementcorresponding to each image style is 1 is first generated, and thequantity of times of using the sticker that is of each image style isadded to the initialized interest vector, to obtain the vector throughaddition and perform normalization processing, to prevent the vectorfrom being excessively sparse due to excessive values of 0 in theinterest vector of the user, thereby improving the accuracy of thesubsequent calculation.

In addition, according to an embodiment of the apparatus provided in thepresent disclosure, when the recommendation index of the specifiedsticker is corrected, impact of an average quantity of times of usingthe stickers by the user per unit of time period and interestcentralization of the user on the correction is comprehensivelyconsidered, thereby improving accuracy of correction of therecommendation index.

In addition, according to an embodiment of the apparatus provided in thepresent disclosure, the image feature information of the sample stickerscorresponding to each image style is obtained; machine-learning trainingis performed on the image feature information and the image stylecorresponding to the image feature information, to obtain themachine-learning classification model; and the image feature informationof the unclassified sticker is input into the machine-learningclassification model, to obtain the image style corresponding to theunclassified sticker, thereby implementing automatic classification ofthe image style of each group of stickers.

It is noted that the embodiments in this specification are all describedin a progressive manner. Descriptions of each embodiment focus ondifferences from other embodiments, and for the same or similar partsamong respective embodiments, refer to each other. The apparatusembodiments are substantially similar to the method embodiments andtherefore are only briefly described, and for the associated part, referto the descriptions of the method embodiments.

The embodiments disclosed above are described to enable persons skilledin the art to implement or use the present disclosure. Variousmodifications to these embodiments can be made, and the generalprinciples defined in the present disclosure may be implemented in otherembodiments without departing from the spirit and scope of the presentdisclosure. Therefore, the present disclosure is not limited to theseembodiments illustrated in the present disclosure, but needs to conformto the broadest scope consistent with the principles and novel featuresdisclosed in the present disclosure.

Based on the sticker recommendation methods described in FIG. 9 to FIG.12, an embodiment of this application further provides a server. Theserver may include a sticker recommendation apparatus described above.

FIG. 16 is a structural block diagram of hardware of a server. A server1600 includes a CPU 1601, a system memory 1604 including a random accessmemory (RAM) 1602 and a read-only memory (ROM) 1603, and a system bus1605 connecting the system memory 1604 and the CPU 1601. The server 1600further includes a basic input/output system (I/O system) 1606 helpingtransmit information between components in a computer, and a massstorage device 1607 configured to store an operating system 1613, anapplication program 1614, and another program module 1615.

The basic input/output system 1606 includes a display 1608 configured todisplay information and an input device 1609, such as a mouse and akeyboard, configured to input information for a user. The display 1608and the input device 1609 are both connected to the CPU 1601 by using aninput and output controller 1610 that is connected to the system bus1605. The basic input/output system 1606 may further include the inputand output controller 1610 configured to receive and process an inputfrom a plurality of other devices such as the keyboard, the mouse, or anelectronic stylus. Similarly, the input and output controller 1610further provides an output to a display screen, a printer or anothertype of output device.

The mass storage device 1607 is connected to the CPU 1601 by using amass storage controller (not shown) that is connected to the system bus1605. The mass storage device 1607 and a computer-readable mediumassociated with the mass storage device 1607 provide non-volatilestorage for the server 1600. That is, the mass storage device 1607 mayinclude a computer-readable medium (not shown) such as a hard disk or aCD-ROM drive.

Without loss of generality, the computer-readable medium may include acomputer storage medium and a communications medium. The computerstorage medium includes volatile and non-volatile media, and removableand non-removable media implemented by using a method or technology forstoring information such as a computer-readable instruction, a datastructure, a program module or other data. The computer storage mediumincludes a RAM, a ROM, an EPROM, an EEPROM, a flash memory or othersolid storage technologies; a CD-ROM, a DVD or other optical storages;and a cassette, a magnetic tape, a disk storage or other magneticstorage devices. It is noted that persons skilled in the art may learnthat the computer storage medium is not limited to the foregoing severaltypes. The system memory 1604 and the mass storage device 1607 may becollectively referred to as a memory.

According to various embodiments of the present disclosure, the server1600 may further be connected to a remote computer on a network throughthe network such as the Internet to run. That is, the server 1600 may beconnected to a network 1612 by using a network interface unit 1611 thatis connected on the system bus 1605, or may be connected to a network ofanother type or a remote computer system (not shown) by using thenetwork interface unit 1611.

The memory further includes one or more programs. The one or moreprograms are stored in the memory. The CPU 1601 executes the one or moreprograms to implement the sticker recommendation method shown in FIG. 9or FIG. 12.

In an embodiment, a non-temporary computer-readable storage mediumincluding an instruction, for example, a memory including aninstruction, is further provided, and the instruction may be executed bya processor in a server to complete the sticker recommendation method ineach embodiment of the present disclosure. For example, thenon-transitory computer-readable storage medium may be a ROM, a RAM, aCD-ROM, a magnetic tape, a floppy disk, or an optical data storagedevice.

Persons skilled in the art may clearly understand that, for the purposeof convenient and brief description, for a specific working process ofthe foregoing system, apparatus, and unit, refer to the correspondingprocess in the foregoing method embodiments, and details are notdescribed herein again.

In the several embodiments provided in this application, it isunderstood that the disclosed system, apparatus, and method may beimplemented in other manners. For example, the described apparatusembodiment is merely an example. For example, the unit division is notlimited to a logical function division and may be other division in anactual implementation. For example, a plurality of units or componentsmay be combined or integrated into another system, or some features maybe ignored or not performed. In addition, the displayed or discussedmutual couplings or direct couplings or communication connections may beimplemented by using some interfaces. The indirect couplings orcommunication connections between the apparatuses or units may beimplemented in electronic, mechanical, or other forms.

The units described as separate parts may or may not be physicallyseparate, and parts displayed as units may or may not be physical units,may be located in one position, or may be distributed on a plurality ofnetwork units. Some or all of the units may be selected according toactual requirements to achieve the objectives of the solutions of theembodiments.

In addition, functional units in the embodiments of this application maybe integrated into one processing unit, or each of the units may existalone physically, or two or more units are integrated into one unit. Theintegrated unit may be implemented in the form of hardware, or may beimplemented in the form of a software functional unit.

When the integrated unit is implemented in the form of a softwarefunctional unit and sold or used as an independent product, theintegrated unit may be stored in a computer-readable storage medium(e.g., a non-transitory computer-readable storage medium). Based on suchan understanding, the technical solutions of this applicationessentially, or the part contributing to the prior art, or all or someof the technical solutions may be implemented in a form of a softwareproduct. The software product is stored in a storage medium, andincludes several instructions for instructing a computer device (whichmay be a personal computer, a server, a network device, or the like) toperform all or some of the steps of the methods described in theembodiments of this application. The storage medium includes a mediumthat can store program code, such as a USB flash drive, a removable harddisk, a ROM, a RAM, a magnetic disk, or an optical disc.

The foregoing embodiments are intended to describe the technicalsolutions of this application, but not to limit this application.Although this application is described in detail with reference to theforegoing embodiments, persons of ordinary skill in the art canunderstand that they may still make modifications to the technicalsolutions set forth in the foregoing embodiments or make equivalentreplacements to some technical features thereof, without departing fromthe spirit and scope of the technical solutions of the embodiments ofthis application.

What is claimed is:
 1. A sticker recommendation method in a server, themethod comprising: receiving, by interface circuitry of the server, asticker recommendation request from a terminal device; determining, byprocessing circuitry of the server, a historical sticker set thatincludes a sticker previously sent by a user of the terminal device, andat least one recommendable sticker set not including the historicalsticker set; determining, by the processing circuitry, a recommendationindex for each of the at least one recommendable sticker set accordingto an emotion feature of the historical sticker set and an emotionfeature of the respective recommendable sticker set; and sending, by theinterface circuitry, a sticker set recommendation for one or more of theat least one recommendable sticker set to the terminal device accordingto the recommendation index for each of the at least one recommendablesticker set.
 2. The method according to claim 1, wherein when the atleast one recommendable sticker set includes a plurality ofrecommendable sticker sets, the determining the recommendation indexfurther comprises: calculating, by the processing circuitry, asimilarity between each of the recommendable sticker sets and thehistorical sticker set according to the emotion feature of therespective recommendable sticker set and the emotion feature of thehistorical sticker set; and determining, by the processing circuitry,recommendation indexes of the plurality of recommendable sticker setsaccording to the similarities between the plurality of recommendablesticker sets and the historical sticker set.
 3. The method according toclaim 1, wherein the emotion feature of the historical sticker set isdetermined according to an emotion feature of the sticker in thehistorical sticker set, the emotion feature of the sticker is a featureextracted from the sticker for reflecting an emotion status presented bythe sticker.
 4. The method according to claim 3, wherein the emotionfeature of the historical sticker set is determined by determining, bythe processing circuitry, an emotion feature of each sticker in thehistorical sticker set; and determining, by the processing circuitry,the emotion feature of the historical sticker set based on an averagevalue of the emotion features of the stickers in the historical stickerset.
 5. The method according to claim 3, wherein the emotion feature ofthe historical sticker set is determined by determining, by theprocessing circuitry, a total quantity of usage times of each sticker inthe historical sticker set; selecting, by the processing circuitry, aspecified quantity of stickers from the historical sticker set based onthe total quantities of usage times; determining, by the processingcircuitry, a weight of each selected sticker of the specified quantityof stickers according to the total quantity of usage times of therespective selected sticker; performing, by the processing circuitry, aweighted summation on emotion features of the selected stickersaccording to the weights of the selected stickers; and determining, bythe processing circuitry, the emotion feature of the historical stickerset based on a result of the weighted summation.
 6. The method accordingto claim 2, wherein the determining the recommendation indexes furthercomprises: calculating, by the processing circuitry, a comprehensivesimilarity score of each of the recommendable sticker sets according tothe similarity between the recommendable sticker sets and the historicalsticker set; and determining, by the processing circuitry, therecommendation indexes of the plurality of recommendable sticker setsaccording to the comprehensive similarity scores of the plurality ofrecommendable sticker sets.
 7. The method according to claim 6, whereinthe calculating the comprehensive similarity score further comprises:summing, by the processing circuitry, similarities between one of theplurality of recommendable sticker sets and each of a plurality ofhistorical sticker sets, the plurality of historical sticker setsincluding the historical sticker set; and determining, by the processingcircuitry, the comprehensive similarity score of the one of theplurality of recommendable sticker sets based on the summedsimilarities.
 8. The method according to claim 1, wherein the at leastone recommendable sticker set includes a plurality of recommendablestocker set, and the determining the at least one recommendable stickerset further includes: clustering, by the processing circuitry andaccording to a plurality of emotion features of the plurality ofrecommendable sticker sets, the plurality of recommendable sticker setsinto a plurality of classifications such that each of the plurality ofrecommendable sticker sets corresponds to one of the plurality ofclassifications and each of the plurality of classifications includes atleast one of the plurality of recommendable sticker sets; determining,by the processing circuitry, a classification to which the historicalsticker set belongs in the plurality of classifications; and selecting,by the processing circuitry, the at least one recommendable sticker setfrom the at least one of the plurality of recommendable sticker setsincluded in the classification to which the historical sticker setbelongs.
 9. The method according to claim 1, further comprising:receiving, by the interface circuitry, a to-be-posted sticker set;extracting, by the processing circuitry, an emotion feature of theto-be-posted sticker set; calculating, by the processing circuitry, asimilarity between the to-be-posted sticker set and each stored stickerset; determining, by the processing circuitry, whether each of thesimilarities between the to-be-posted sticker set and each storedsticker set is less than a preset threshold; and storing, by theprocessing circuitry, the to-be-posted sticker set when each of thesimilarities between the to-be-posted sticker set and each storedsticker set is determined to be less than the preset threshold.
 10. Themethod according to claim 1, further comprising: when the emotionfeature indicates an image style of the sticker in the historicalsticker set, one sticker in the at least one recommendable sticker is aspecified sticker, each sticker in the historical sticker set has animage style such that the historical sticker set has at least one imagestyle, determining, by the processing circuitry, a usage record of thehistorical sticker set by the user; determining, by the processingcircuitry, an uncorrected recommendation index of the specified stickerand an image style of the specified sticker; determining, by theprocessing circuitry, a corrected recommendation index of the specifiedsticker, according to the usage record of the historical sticker set bythe user, the at least one image style of the historical sticker set,the uncorrected recommendation index of the specified sticker, and theimage style of the specified sticker; and sending, by the interfacecircuitry, the specified sticker to the terminal device when thecorrected recommendation index of the specified sticker satisfies arecommendation condition.
 11. The method according to claim 10, whereinthe determining the corrected recommendation index further comprises:generating, by the processing circuitry, an interest vector of the useraccording to the usage record of the historical sticker set by the userand the at least one image style of the historical sticker set such thateach of the at least one image style of the historical sticker setcorresponds to an element of the interest vector; and determining, bythe processing circuitry, the corrected recommendation index of thespecified sticker according to the interest vector of the user, theimage style of the specified sticker, and a preset correction formulaincluding the uncorrected recommended index of the specified sticker.12. The method according to claim 11, wherein, when the usage recordincludes a quantity of usage times of the historical sticker set by theuser, the generating the interest vector of the user comprises:generating, by the processing circuitry, an initialized interest vectoraccording to a quantity of image styles including the at least one imagestyle of the historical sticker set such that each element of theinitialized interest vector corresponds to one of the quantity of imagestyles; adding, by the processing circuitry and for each of the at leastone image style of the historical sticker set, the quantity of usagetimes of the historical sticker set by the user to an element of theinitialized interest vector that corresponds to the respective imagestyle; and generating, by the processing circuitry, the interest vectorof the user according to a normalization of the initialized interestvector.
 13. The method according to claim 11, wherein the presetcorrection formula is:${{{rp}\left( {u,e} \right)} = {{{cf}\left( {u,e} \right)}*\left( {1 + {\left( {\frac{2}{1 + {\exp \left( {{- 0.1}*{{frq}(u)}} \right)}} - 1} \right)*{{var}\left( {v(u)} \right)}*{{sim}\left( {e,{v(u)}} \right)}}} \right)}},$wherein rp(u,e) is the corrected recommendation index, cf(u,e) is theuncorrected recommendation index, frq(u) is an average quantity of usagetimes of the historical stickers by the user per unit of time period,v(u) is the interest vector of the user, var(v(u)) is a variance valueof each element in the interest vector, and sim(e,v(u)) is a cosinesimilarity between the specified sticker and the interest vector of theuser.
 14. The method according to claim 10, wherein the determining theuncorrected recommendation index further comprises: determining, by theprocessing circuitry, the uncorrected recommendation index of thespecified sticker according to a collaborative-filtering-basedrecommendation algorithm.
 15. The method according to claim 10, whereinthe recommendation condition comprises at least one of the followingconditions: the corrected recommendation index is greater than a presetindex threshold; and a rank of the corrected recommendation index in therecommendation indexes of the at least one recommendable sticker set isgreater than a preset rank threshold.
 16. The method according to claim10, wherein the image style of the historical sticker in the historicalsticker set is determined by: extracting, by the processing circuitry,image feature information of a plurality of sample stickers and thehistorical sticker, the image feature information of the plurality ofsample stickers corresponding to a plurality of image styles;determining, by the processing circuitry, a machine-learningclassification model according to the image feature information of theplurality of sample stickers and the plurality of image styles; anddetermining, by the processing circuitry, the image style of thehistorical sticker according to an output of the machine-learningclassification model with an input of image feature information of thehistorical sticker.
 17. A sticker recommendation apparatus, comprising:interface circuitry configured to receive a sticker recommendationrequest from a terminal; and processing circuitry configured todetermine a historical sticker set that includes a sticker previouslysent by a user of the terminal device, and at least one recommendablesticker set not including the historical sticker set; determine arecommendation index for each of the at least one recommendable stickerset according to an emotion feature of the historical sticker set and anemotion feature of the respective recommendable sticker set; and send,via the interface circuitry, a sticker set recommendation for one ormore of the at least one recommendable sticker set to the terminaldevice according to the recommendation index for each of the at leastone recommendable sticker set.
 18. The sticker recommendation apparatusaccording to claim 17, wherein when the at least one recommendablesticker set includes a plurality of recommendable sticker sets, theprocessing circuitry is further configured to: calculate a similaritybetween each of the recommendable sticker sets and the historicalsticker set according to the emotion feature of the respectiverecommendable sticker set and the emotion feature of the historicalsticker set; and determine recommendation indexes of the plurality ofrecommendable sticker sets according to the similarities between theplurality of recommendable sticker sets and the historical sticker set.19. The sticker recommendation apparatus according to claim 17, whereinthe emotion feature of the historical sticker set is determinedaccording to an emotion feature of the sticker in the historical stickerset, the emotion feature of the sticker is a feature extracted from thesticker for reflecting an emotion status presented by the sticker. 20.The sticker recommendation apparatus according to claim 19, wherein theprocessing circuitry is further configured to: determine an emotionfeature of each sticker in the historical sticker set; and determine theemotion feature of the historical sticker set based on an average valueof the emotion features of the stickers in the historical sticker set.21. The sticker recommendation apparatus according to claim 19, whereinthe processing circuitry is further configured to: determine a totalquantity of usage times of each sticker in the historical sticker set;select a specified quantity of stickers from the historical sticker setbased on the total quantities of usage times; determine a weight of eachselected sticker of the specified quantity of stickers according to thetotal quantity of usage times of the respective selected sticker;perform a weighted summation on emotion features of the selectedstickers according to the weights of the selected stickers; anddetermine the emotion feature of the historical sticker set based on aresult of the weighted summation.
 22. The sticker recommendationapparatus according to claim 18, wherein the processing circuitry isfurther configured to: calculate a comprehensive similarity score ofeach of the recommendable sticker sets according to the similaritybetween the recommendable sticker sets and the historical sticker set;and determine the recommendation indexes of the plurality ofrecommendable sticker sets according to the comprehensive similarityscores of the plurality of recommendable sticker sets.
 23. The stickerrecommendation apparatus according to claim 22, wherein the processingcircuitry is further configured to: sum similarities between one of theplurality of recommendable sticker sets and each of a plurality ofhistorical sticker sets, the plurality of historical sticker setsincluding the historical sticker set; and determine the comprehensivesimilarity score of the one of the plurality of recommendable stickersets based on the summed similarities.
 24. The sticker recommendationapparatus according to claim 17, wherein the at least one recommendablesticker set includes a plurality of recommendable stocker set, and theprocessing circuitry is further configured to: cluster, according to aplurality of emotion features of the plurality of recommendable stickersets, the plurality of recommendable sticker sets into a plurality ofclassifications such that each of the plurality of recommendable stickersets corresponds to one of the plurality of classifications and each ofthe plurality of classifications includes at least one of the pluralityof recommendable sticker sets; determine a classification to which thehistorical sticker set belongs in the plurality of classifications; andselect the at least one recommendable sticker set from the at least oneof the plurality of recommendable sticker sets included in theclassification to which the historical sticker set belongs.
 25. Thesticker recommendation apparatus according to claim 17, wherein theprocessing circuitry is further configured to: receive a to-be-postedsticker set; extract an emotion feature of the to-be-posted sticker set;calculate a similarity between the to-be-posted sticker set and eachstored sticker set; determine whether each of the similarities betweenthe to-be-posted sticker set and each stored sticker set is less than apreset threshold; and store the to-be-posted sticker set when each ofthe similarities between the to-be-posted sticker set and each storedsticker set is determined to be less than the preset threshold.
 26. Thesticker recommendation apparatus according to claim 17, wherein theprocessing circuitry is further configured to: when the emotion featureindicates an image style of the sticker in the historical sticker set,one sticker in the at least one recommendable sticker is a specifiedsticker, each sticker in the historical sticker set has an image stylesuch that the historical sticker set has at least one image style,determine a usage record of the historical sticker set by the user;determine an uncorrected recommendation index of the specified stickerand an image style of the specified sticker; determine a correctedrecommendation index of the specified sticker, according to the usagerecord of the historical sticker set by the user, the at least one imagestyle of the historical sticker set, the uncorrected recommendationindex of the specified sticker, and the image style of the specifiedsticker; and send, via the interface circuitry, the specified sticker tothe terminal device when the corrected recommendation index of thespecified sticker satisfies a recommendation condition.
 27. The stickerrecommendation apparatus according to claim 26, wherein the processingcircuitry is further configured to: generate an interest vector of theuser according to the usage record of the historical sticker set by theuser and the at least one image style of the historical sticker set suchthat each of the at least one image style of the historical sticker setcorresponds to an element of the interest vector; and determine thecorrected recommendation index of the specified sticker according to theinterest vector of the user, the image style of the specified sticker,and a preset correction formula including the uncorrected recommendedindex of the specified sticker.
 28. The sticker recommendation apparatusaccording to claim 27, wherein the processing circuitry is furtherconfigured to: when the usage record includes a quantity of usage timesof the historical sticker set by the user, generate an initializedinterest vector according to a quantity of image styles including the atleast one image style of the historical sticker set such that eachelement of the initialized interest vector corresponds to one of thequantity of image styles; add, for each of the at least one image styleof the historical sticker set, the quantity of usage times of thehistorical sticker set by the user to an element of the initializedinterest vector that corresponds to the respective image style; andgenerate the interest vector of the user according to a normalization ofthe initialized interest vector.
 29. The sticker recommendationapparatus according to claim 26, wherein the processing circuitry isfurther configured to: determine the uncorrected recommendation index ofthe specified sticker according to a collaborative-filtering-basedrecommendation algorithm.
 30. The sticker recommendation apparatusaccording to claim 26, wherein the processing circuitry is furtherconfigured to: extract image feature information of a plurality ofsample stickers and the historical sticker, the image featureinformation of the plurality of sample stickers corresponding to aplurality of image styles; determine a machine-learning classificationmodel according to the image feature information of the plurality ofsample stickers and the plurality of image styles; and determine theimage style of the historical sticker according to an output of themachine-learning classification model with an input of image featureinformation of the historical sticker.
 31. A non-transitorycomputer-readable storage medium storing a program executable by atleast one processor to perform: receiving a sticker recommendationrequest from a terminal device; determining a historical sticker setthat includes a sticker previously sent by a user of the terminaldevice, and at least one recommendable sticker set not including thehistorical sticker set; determining a recommendation index for each ofthe at least one recommendable sticker set according to an emotionfeature of the historical sticker set and an emotion feature of therespective recommendable sticker set; and sending a sticker setrecommendation for one or more of the at least one recommendable stickerset to the terminal device according to the recommendation index foreach of the at least one recommendable sticker set.